Untitled
Kaique
25/10/2020
Necessary packages
library(tidyverse)
library(FIELDimageR)
library(raster)
library(readxl)
library(gsheet)
library(foreach)
library(agricolae)
library(reshape2)
library(cowplot)
library(gbm)
library(lime)
library(lme4)
library(DescTools)
library(factoextra)
library(FactoMineR)
library(corrplot)
library(magick)
library(patchwork)
library(forcats)
library(ggdist)Example of RGB indices for one image
Load pictures names
pics<-list.files("./pics/01-Calonectria_leaf_bligth")
length(pics)## [1] 300# write(pics, "pics_names.txt")List of RGB-based spectral indices to be calculated in the image
# Vegetation indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")Load a single image
#Choose one image to prepare the pipeline
EX.L1<-stack(paste("./pics/01-Calonectria_leaf_bligth/",pics[254],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot =T)## [1] "3 layers available"
# plotRGB(EX.L.Shape)EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Blue"), plot = T)## [1] "3 layers available"
plot(EX1.Indices$Blue)
hist(EX1.Indices$Blue)
mask
# EX.L2<-fieldMask(mosaic=EX.L1, myIndex = "Red", cropValue=200, cropAbove=T, plot = T)
# EX.L2<-fieldMask(mosaic=EX.L1, index = "BI", cropValue=1, cropAbove=F, plot = T)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=175, cropAbove=T, plot = T)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
# plotRGB(EX.L2$newMosaic)
plotRGB(EX.L2$newMosaic)
class(EX.L2$mask)## [1] "RasterStack"
## attr(,"package")
## [1] "raster"rgb_fig = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_void()+
coord_fixed()## Coordinate system already present. Adding new coordinate system, which will replace the existing one.cut = mask(EX.L1, EX.L2$newMosaic)
plot(cut)
EX.L4<-fieldIndex(mosaic=cut,
index = index)## [1] "3 layers available"
plot(EX.L4$BGI)
gli_fig = as.data.frame(EX.L4$BGI, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = BGI))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_void()+
coord_fixed()plot_grid(rgb_fig, gli_fig, axis = "b",
rel_widths = c(0.9,1),
labels = c("RGB", "BGI"),
scale = 0.90)
# ggsave("figs/leaf_gli.png",dpi = 600, height = 3, width =10 )df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
gather(1:(3+length(index)), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[30])## `summarise()` ungrouping output (override with `.groups` argument)Wheat leaf blast
pics<-list.files("./pics/01-Wheat_leaf_blast")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-Wheat_leaf_blast/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=10)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=180, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_WLB.txt")box = read.table("data/indexes_WLB.txt")Load severity data
# sev_data = gsheet2tbl("https://docs.google.com/spreadsheets/d/106sg_O8DeALZpWnxERQNgk8UKgsFbhobWwrl4JXOzbo/edit#gid=0")
sev = gsheet2tbl("https://docs.google.com/spreadsheets/d/106sg_O8DeALZpWnxERQNgk8UKgsFbhobWwrl4JXOzbo/edit#gid=0")
sev## # A tibble: 200 x 5
## inoculated code rep pic_name sev
## <chr> <chr> <dbl> <chr> <dbl>
## 1 BRS Guamirim 42 1 G_42_R1 0.18
## 2 BRS Guamirim 42 2 G_42_R2 0.38
## 3 BRS Guamirim 42 3 G_42_R3 0
## 4 BRS Guamirim 42 4 G_42_R4 0.05
## 5 BRS Guamirim 108 1 G_108_R1 0.2
## 6 BRS Guamirim 108 2 G_108_R2 0.9
## 7 BRS Guamirim 108 3 G_108_R3 0.28
## 8 BRS Guamirim 108 4 G_108_R4 0.14
## 9 BRS Guamirim 110 1 G_110_R1 0.19
## 10 BRS Guamirim 110 2 G_110_R2 0.21
## # ... with 190 more rowsblast_data = gsheet2tbl("https://docs.google.com/spreadsheets/d/1KnJ9N8jqKPMjCt8jCLv7OqYL9hLyYeK8wkGEBmlGSVI/edit?usp=sharing") %>%
dplyr::select(code, city, region, position_wheat, host,species)
# blast_data
sev_data = full_join(sev, blast_data, by = "code") %>%
mutate(n = seq(1:1123)) %>%
filter(n<201) %>%
dplyr::select(-n) %>%
dplyr::select(pic_name, sev)
# sev_data
length(unique(sev$pic_name))## [1] 200all_data = box %>%
separate(leaf, into = c("pic_name", "jpg"), sep = ".jpg") %>%
# separate(pic_name, into = c("hh", "isolate","repp"), sep = "_", remove = F) %>%
dplyr::select(-jpg) %>%
full_join(sev_data) %>%
# filter(sev>0) %>%
mutate(sev = case_when(sev==0 ~0.01,
sev >0 ~sev)) %>%
filter(!is.na(index)) %>%
mutate(sev = sev)## Joining, by = "pic_name"length(unique(all_data$pic_name))## [1] 200# length(unique(box$leaf))summary(sev$sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.6375 49.6250 44.0737 76.0050 99.9300Images
hist_sev_WLB = sev %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))
hist_sev_WLB## Warning: Removed 2 rows containing missing values (geom_bar).
EX.L1<-stack(paste("./pics/01-Wheat_leaf_blast/","G_758_R2.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=10)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)## [1] "3 layers available"EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)## [1] "3 layers available"EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=180, cropAbove=T, plot = F)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)## [1] "3 layers available"# plot(EX.L4$NGRDI)rgb_fig_wlb = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))## Coordinate system already present. Adding new coordinate system, which will replace the existing one.gli_fig_wlb = as.data.frame(EX.L4$VARI, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = VARI))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))# plot_grid(rgb_fig, gli_fig, axis = "b",
# rel_widths = c(0.9,1),
# labels = c("RGB", "NGRDI"),
# scale = 0.90)
rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB## Warning: Removed 2 rows containing missing values (geom_bar).
# ggsave("figs/leaf_gli_wlb.png",dpi = 600, height = 4, width =12)Relationship sev indices
rgb_gg = all_data %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg = all_data %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg,
nrow = 2,
rel_heights = c(0.5,1))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_WLB.png", dpi = 500, height = 8, width = 10)cor_wlb = all_data %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = cor.test(mean,sev, method = "spearman")$p.value) %>%
arrange(-cor)## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties## `summarise()` ungrouping output (override with `.groups` argument)cor_wlb## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 SCI 0.867 1.14e-61
## 2 HI 0.823 1.70e-50
## 3 Blue 0.822 2.70e-50
## 4 HUE 0.752 9.44e-38
## 5 Red 0.666 5.11e-27
## 6 BI 0.643 1.12e-24
## 7 gray 0.59 4.16e-20
## 8 BGI 0.475 1.22e-12
## 9 Green 0.436 1.07e-10
## 10 SI 0.382 2.33e- 8
## 11 NGRDI -0.867 1.14e-61
## 12 VARI -0.87 1.06e-62
## 13 GLI -0.886 4.53e-68Spread
all_data_spread = all_data %>%
# mutate(nn=1:length(all_data$pic_name)) %>%
pivot_wider(id_col = c(pic_name,sev),
names_from = index,
values_from = mean)
all_data_spread## # A tibble: 200 x 15
## pic_name sev BGI BI Blue GLI gray Green HI HUE NGRDI
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 G_108_R1 0.2 0.535 107. 71.7 0.193 118. 131. 0.218 -1.56 0.0970
## 2 G_108_R2 0.9 0.646 103. 79.2 0.148 112. 122. 0.0459 -1.50 0.0872
## 3 G_108_R3 0.28 0.678 99.3 79.4 0.137 107. 116. -0.0920 -1.55 0.0858
## 4 G_108_R4 0.14 0.649 104. 79.8 0.148 112. 123. -0.0500 -1.56 0.0885
## 5 G_110_R1 0.19 0.651 102. 79.3 0.148 111. 122. 0.0730 -1.56 0.0903
## 6 G_110_R2 0.21 0.638 104. 79.1 0.151 113. 124. 0.0666 -1.56 0.0867
## 7 G_110_R3 0.3 0.645 107. 81.9 0.144 116. 127. 0.112 -1.56 0.0797
## 8 G_110_R4 0.08 0.694 98.1 79.7 0.133 105. 115. -0.164 -1.56 0.0887
## 9 G_112_R1 0.01 0.645 104. 80.1 0.150 113. 124. -0.00156 -1.56 0.0902
## 10 G_112_R2 0.06 0.690 99.4 80.5 0.134 107. 116. -0.170 -1.56 0.0874
## # ... with 190 more rows, and 4 more variables: Red <dbl>, SCI <dbl>, SI <dbl>,
## # VARI <dbl>GBM
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 52.
## There were 13 predictors of which 13 had non-zero influence.sqrt(min(gbm.fit$cv.error))## [1] 9.034891gbm.perf(gbm.fit, method = "cv")
## [1] 52# find index for n trees with minimum CV errormin_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])## [1] 9.034891# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )## Using 52 trees...sqrt(mean(((pred)-all_data_spread$sev[-train])^2))## [1] 11.15379CCC((pred), all_data_spread$sev[-train])$rho.c$est## [1] 0.9461264plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Testing various hyperparameters
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)## [1] 192# randomize data
set.seed(1234)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray +Red+Green+Blue + SI + SCI, #<<<<<
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC(pred, all_data_spread$sev[-train])$rho.c$est#<<<<<
}## Using 5000 trees...## Using 606 trees...## Using 54 trees...## Using 20 trees...## Using 3601 trees...## Using 323 trees...## Using 29 trees...## Using 9 trees...## Using 3242 trees...## Using 328 trees...## Using 36 trees...## Using 19 trees...## Using 3225 trees...## Using 331 trees...## Using 39 trees...## Using 7 trees...## Using 4999 trees...## Using 1013 trees...## Using 58 trees...## Using 31 trees...## Using 3612 trees...## Using 323 trees...## Using 31 trees...## Using 9 trees...## Using 3378 trees...## Using 328 trees...## Using 31 trees...## Using 71 trees...## Using 3378 trees...## Using 328 trees...## Using 31 trees...## Using 71 trees...## Using 4999 trees...## Using 945 trees...## Using 91 trees...## Using 23 trees...## Using 4766 trees...## Using 550 trees...## Using 34 trees...## Using 14 trees...## Using 4766 trees...## Using 550 trees...## Using 34 trees...## Using 14 trees...## Using 4766 trees...## Using 550 trees...## Using 34 trees...## Using 14 trees...## Using 4995 trees...## Using 591 trees...## Using 50 trees...## Using 27 trees...## Using 3610 trees...## Using 385 trees...## Using 38 trees...## Using 11 trees...## Using 3304 trees...## Using 5000 trees...## Using 34 trees...## Using 69 trees...## Using 3205 trees...## Using 4970 trees...## Using 32 trees...## Using 7 trees...## Using 4995 trees...## Using 938 trees...## Using 2711 trees...## Using 375 trees...## Using 3493 trees...## Using 336 trees...## Using 31 trees...## Using 53 trees...## Using 3349 trees...## Using 334 trees...## Using 31 trees...## Using 11 trees...## Using 3349 trees...## Using 324 trees...## Using 31 trees...## Using 11 trees...## Using 4996 trees...## Using 789 trees...## Using 50 trees...## Using 446 trees...## Using 3659 trees...## Using 389 trees...## Using 38 trees...
##
## Using 38 trees...## Using 3659 trees...## Using 389 trees...## Using 38 trees...
##
## Using 38 trees...## Using 3659 trees...## Using 389 trees...## Using 38 trees...
##
## Using 38 trees...## Using 4999 trees...## Using 739 trees...## Using 91 trees...## Using 27 trees...## Using 3636 trees...## Using 391 trees...## Using 29 trees...## Using 82 trees...## Using 3606 trees...## Using 4988 trees...## Using 1606 trees...## Using 70 trees...## Using 3527 trees...## Using 5000 trees...## Using 1913 trees...## Using 12 trees...## Using 4999 trees...## Using 739 trees...## Using 54 trees...## Using 27 trees...## Using 3648 trees...## Using 4999 trees...## Using 4511 trees...## Using 11 trees...## Using 3653 trees...## Using 4999 trees...## Using 4985 trees...## Using 13 trees...## Using 3540 trees...## Using 4967 trees...## Using 4417 trees...## Using 13 trees...## Using 5000 trees...## Using 630 trees...## Using 3637 trees...## Using 28 trees...## Using 3450 trees...## Using 336 trees...## Using 29 trees...## Using 12 trees...## Using 3378 trees...## Using 341 trees...## Using 29 trees...## Using 10 trees...## Using 3378 trees...## Using 341 trees...## Using 29 trees...## Using 10 trees...## Using 5000 trees...## Using 1017 trees...## Using 122 trees...## Using 130 trees...## Using 4663 trees...## Using 456 trees...## Using 48 trees...## Using 10 trees...## Using 3029 trees...## Using 378 trees...## Using 35 trees...## Using 9 trees...## Using 3539 trees...## Using 301 trees...## Using 27 trees...## Using 9 trees...## Using 5000 trees...## Using 4997 trees...## Using 953 trees...## Using 28 trees...## Using 3651 trees...## Using 5000 trees...## Using 4990 trees...## Using 4906 trees...## Using 3384 trees...## Using 337 trees...## Using 5000 trees...## Using 10 trees...## Using 3367 trees...## Using 343 trees...## Using 33 trees...## Using 142 trees...## Using 4999 trees...## Using 622 trees...## Using 90 trees...## Using 20 trees...## Using 3456 trees...## Using 350 trees...## Using 43 trees...## Using 13 trees...## Using 3195 trees...## Using 318 trees...## Using 32 trees...## Using 10 trees...## Using 3220 trees...## Using 320 trees...## Using 31 trees...## Using 10 trees...best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.01 5 5 0.80 4988
## 2 0.10 5 5 0.50 36
## 3 0.30 5 5 0.50 19
## 4 0.01 6 5 0.80 5000
## 5 0.30 3 10 0.80 11
## 6 0.01 6 5 0.50 331
## 7 0.01 5 5 0.65 5000
## 8 0.01 6 5 0.65 4970
## 9 0.10 5 10 0.80 4985
## 10 0.30 5 10 1.00 10
## min_RMSE CCC
## 1 10.83493 0.9731869
## 2 10.88780 0.9730593
## 3 11.04311 0.9729481
## 4 10.70554 0.9722267
## 5 10.84073 0.9716871
## 6 10.34085 0.9716634
## 7 10.62293 0.9713993
## 8 10.52067 0.9713708
## 9 10.54148 0.9711163
## 10 11.28268 0.9709846# gbm.tune$fitBest model
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+Red+Green+Blue+gray+SI+SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)Relative influence
par(mar = c(5, 8, 1, 1))
summary_gbm_wlb = summary(
gbm.fit.final,
cBars = 15,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_wlb = summary_gbm_wlb %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_wlb
ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "NGRDI", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( NGRDI,(yhat)))+
geom_line()
LIME
model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
Prediction
# predict values for test data
pred <- predict(gbm.fit.final,
n.trees = gbm.fit.final$n.trees,
all_data_spread[-train,])
# results
caret::RMSE((pred), all_data_spread[-train,]$sev)## [1] 7.852842CCC((pred), all_data_spread$sev[-train])$rho.c$est## [1] 0.9733397cor((pred), all_data_spread$sev[-train])^2## [1] 0.9491022accuracy_wlb =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_wlb## RMSE r s.shift l.shift C.b CCC CIS
## 1 7.852842 0.9742187 1.030742 0.02982214 0.9990977 0.9733397 0.95, 0.99plot
# plot_grid(
conc_wlb = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)## `geom_smooth()` using formula 'y ~ x'Soybean Rust
pics<-list.files("./pics/01-soybean-rust-bg-blue")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-soybean-rust-bg-blue/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=100, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_SBR.txt")box = read.table("data/indexes_SBR.txt")load SBR severity
sbr_load = gsheet2tbl("https://docs.google.com/spreadsheets/d/13TVKBQgfCAr7UGie_LHTF_kwPHC1XI_AkLKoRYtjPrQ/edit?usp=sharing")all_data_sbr = box %>%
separate(leaf, into=c("file","format"), sep ="_") %>%
dplyr::select(-format) %>%
full_join(sbr_load) %>%
na.omit()## Joining, by = "file"length(unique(all_data_sbr$file))## [1] 203head(all_data_sbr)## index mean std Q25 Q50 Q75 file
## 1 BGI 0.3997101 0.13599078 0.3234259 0.3843265 0.4506906 Ferrugem 1
## 2 BI 87.8460326 14.91203193 77.1221560 87.0969043 98.8352819 Ferrugem 1
## 3 Blue 41.0937912 11.80079235 33.5510204 40.1326531 46.7959184 Ferrugem 1
## 4 GLI 0.1959277 0.06355385 0.1562426 0.1952057 0.2354938 Ferrugem 1
## 5 gray 96.1659570 15.23897816 85.5722755 95.7584898 107.3897857 Ferrugem 1
## 6 Green 104.4390983 13.30553965 95.8163265 104.3571429 113.8163265 Ferrugem 1
## sev
## 1 39.7
## 2 39.7
## 3 39.7
## 4 39.7
## 5 39.7
## 6 39.7summary(all_data_sbr$sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.04 5.52 20.10 25.11 38.07 89.67Images
hist_sev_sbr = all_data_sbr %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))EX.L1<-stack(paste("./pics/01-soybean-rust-bg-blue/","Ferrugem 2_Median.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)## [1] "3 layers available"EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)## [1] "3 layers available"EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=100, cropAbove=T, plot = F)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)## [1] "3 layers available"# plot(EX.L4$HUE)rgb_fig_sbr = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))## Coordinate system already present. Adding new coordinate system, which will replace the existing one.gli_fig_sbr = as.data.frame(EX.L4$HUE, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = HUE))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))rgb_fig_sbr + gli_fig_sbr + hist_sev_sbr #+## Warning: Removed 2 rows containing missing values (geom_bar).
# rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
# plot_layout(widths = c(1, 1, 1),
# heights = c(1,1))
# ggsave("figs/leaf_gli.png",dpi = 600, height = 6, width =10)Relationship sev indices
rgb_gg_sbr = all_data_sbr %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_sbr## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_sbr = all_data_sbr %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_sbr## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_sbr,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_sbr,
nrow = 2,
rel_heights = c(0.5,1))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_SBR.png", dpi = 500, height = 8, width = 10)cor_sbr = all_data_sbr %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = cor.test(mean,sev, method = "spearman")$p.value) %>%
arrange(-cor)## `summarise()` ungrouping output (override with `.groups` argument)cor_sbr## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 HUE 0.995 0.
## 2 SCI 0.946 0.
## 3 HI 0.906 0.
## 4 Red 0.848 0.
## 5 SI 0.823 0.
## 6 BI 0.745 0.
## 7 gray 0.71 0.
## 8 Green 0.582 0.
## 9 Blue -0.383 2.26e- 8
## 10 GLI -0.462 3.93e-12
## 11 BGI -0.617 0.
## 12 VARI -0.88 0.
## 13 NGRDI -0.946 0.Spread df
all_data_spread = all_data_sbr %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean)
all_data_spread## # A tibble: 203 x 15
## file sev BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Ferr~ 39.7 0.400 87.8 41.1 0.196 96.2 104. 0.663 -0.263 0.0320 101.
## 2 Ferr~ 18.6 0.379 84.7 39.7 0.231 94.0 105. 0.565 -0.953 0.0711 92.9
## 3 Ferr~ 26.4 0.503 90.3 53.1 0.165 98.7 107. 0.746 -0.668 0.0372 100.
## 4 Ferr~ 2.33 0.554 81.2 55.1 0.186 89.3 99.7 0.204 -1.50 0.101 82.0
## 5 Ferr~ 84.7 0.434 113. 51.8 0.116 121. 123. 1.62 1.23 -0.0713 142.
## 6 Ferr~ 4.08 0.505 89.3 55.5 0.196 98.7 110. 0.319 -1.43 0.0876 93.0
## 7 Ferr~ 1.1 0.580 80.8 57.5 0.182 88.6 99.0 0.0999 -1.54 0.108 80.0
## 8 Ferr~ 48.5 0.506 107. 60.3 0.133 116. 122. 1.19 0.0521 -0.0118 125.
## 9 Ferr~ 2.62 0.574 82.6 57.4 0.173 90.6 100. 0.127 -1.49 0.0900 84.1
## 10 Ferr~ 6.92 0.426 112. 57.7 0.199 124. 136. 0.678 -1.31 0.0465 124.
## # ... with 193 more rows, and 3 more variables: SCI <dbl>, SI <dbl>, VARI <dbl>GBM
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 942.
## There were 13 predictors of which 13 had non-zero influence.sqrt(min(gbm.fit$cv.error))## [1] 4.02846gbm.perf(gbm.fit, method = "cv")
## [1] 942# find index for n trees with minimum CV errormin_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])## [1] 4.02846# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )## Using 942 trees...sqrt(mean(((pred)-all_data_spread$sev[-train])^2))## [1] 3.398297CCC((pred), all_data_spread$sev[-train])$rho.c$est## [1] 0.9897837plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Testing various hyperparameters
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)## [1] 192# randomize data
set.seed(123)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.8*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray +Red+Green+Blue + SI + SCI, #<<<<<
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC(pred, all_data_spread$sev[-train])$rho.c$est#<<<<<
}## Using 5000 trees...## Using 2636 trees...## Using 228 trees...## Using 57 trees...## Using 4999 trees...## Using 1734 trees...## Using 178 trees...## Using 357 trees...## Using 5000 trees...## Using 1797 trees...## Using 153 trees...## Using 126 trees...## Using 5000 trees...## Using 1797 trees...## Using 153 trees...## Using 76 trees...## Using 5000 trees...## Using 4969 trees...## Using 591 trees...## Using 297 trees...## Using 5000 trees...## Using 4916 trees...## Using 485 trees...## Using 70 trees...## Using 5000 trees...## Using 4896 trees...## Using 657 trees...## Using 118 trees...## Using 5000 trees...## Using 4896 trees...## Using 657 trees...## Using 118 trees...## Using 5000 trees...## Using 3278 trees...## Using 394 trees...## Using 68 trees...## Using 5000 trees...## Using 2055 trees...## Using 156 trees...## Using 63 trees...## Using 5000 trees...## Using 2055 trees...## Using 156 trees...## Using 63 trees...## Using 5000 trees...## Using 2055 trees...## Using 156 trees...## Using 63 trees...## Using 5000 trees...## Using 1650 trees...## Using 131 trees...## Using 105 trees...## Using 4995 trees...## Using 1904 trees...## Using 98 trees...## Using 27 trees...## Using 5000 trees...## Using 1576 trees...## Using 132 trees...## Using 84 trees...## Using 5000 trees...## Using 1579 trees...## Using 132 trees...## Using 10 trees...## Using 5000 trees...## Using 4943 trees...## Using 576 trees...## Using 134 trees...## Using 5000 trees...## Using 4988 trees...## Using 489 trees...## Using 212 trees...## Using 5000 trees...## Using 4083 trees...## Using 298 trees...## Using 99 trees...## Using 5000 trees...## Using 4129 trees...## Using 424 trees...## Using 214 trees...## Using 5000 trees...## Using 3608 trees...## Using 439 trees...## Using 133 trees...## Using 5000 trees...## Using 2813 trees...## Using 449 trees...## Using 63 trees...## Using 5000 trees...## Using 2813 trees...## Using 449 trees...## Using 59 trees...## Using 5000 trees...## Using 2813 trees...## Using 449 trees...## Using 59 trees...## Using 5000 trees...## Using 1601 trees...## Using 125 trees...## Using 53 trees...## Using 5000 trees...## Using 1148 trees...## Using 78 trees...## Using 15 trees...## Using 5000 trees...## Using 896 trees...## Using 80 trees...## Using 23 trees...## Using 5000 trees...## Using 1161 trees...## Using 105 trees...## Using 17 trees...## Using 5000 trees...## Using 4708 trees...## Using 728 trees...## Using 428 trees...## Using 5000 trees...## Using 4236 trees...## Using 374 trees...## Using 118 trees...## Using 5000 trees...## Using 3092 trees...## Using 301 trees...## Using 102 trees...## Using 5000 trees...## Using 3103 trees...## Using 263 trees...## Using 109 trees...## Using 5000 trees...## Using 4714 trees...## Using 655 trees...## Using 247 trees...## Using 5000 trees...## Using 4756 trees...## Using 499 trees...## Using 127 trees...## Using 5000 trees...## Using 3402 trees...## Using 292 trees...## Using 130 trees...## Using 5000 trees...## Using 3402 trees...## Using 292 trees...## Using 130 trees...## Using 5000 trees...## Using 3297 trees...## Using 355 trees...## Using 1401 trees...## Using 5000 trees...## Using 4600 trees...## Using 612 trees...## Using 20 trees...## Using 4999 trees...## Using 1109 trees...## Using 93 trees...## Using 33 trees...## Using 5000 trees...## Using 565 trees...## Using 97 trees...## Using 27 trees...## Using 5000 trees...## Using 4994 trees...## Using 1345 trees...## Using 297 trees...## Using 5000 trees...## Using 2565 trees...## Using 271 trees...## Using 133 trees...## Using 5000 trees...## Using 2912 trees...## Using 235 trees...## Using 77 trees...## Using 5000 trees...## Using 3010 trees...## Using 246 trees...## Using 104 trees...## Using 5000 trees...## Using 4997 trees...## Using 738 trees...## Using 227 trees...## Using 5000 trees...## Using 4998 trees...## Using 585 trees...## Using 109 trees...## Using 4999 trees...## Using 4259 trees...## Using 385 trees...## Using 149 trees...## Using 5000 trees...## Using 3530 trees...## Using 400 trees...## Using 202 trees...best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.010 3 5 0.80 1148
## 2 0.010 5 5 1.00 1109
## 3 0.100 6 5 1.00 97
## 4 0.100 5 5 1.00 93
## 5 0.010 3 5 1.00 4600
## 6 0.010 1 5 0.80 1601
## 7 0.010 1 5 0.65 1650
## 8 0.001 3 5 0.65 4995
## 9 0.010 6 5 0.80 1161
## 10 0.001 3 5 0.80 5000
## min_RMSE CCC
## 1 2.270680 0.9958594
## 2 2.002059 0.9956714
## 3 1.975201 0.9955144
## 4 2.055946 0.9955131
## 5 2.157053 0.9955040
## 6 2.266556 0.9954981
## 7 2.375012 0.9954573
## 8 2.488595 0.9954234
## 9 2.073971 0.9953818
## 10 2.302626 0.9953742# gbm.tune$fitBest model
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+Red+Green+Blue+gray+SI+SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)Relative influence
par(mar = c(5, 8, 1, 1))
summary_gbm_sbr = summary(
gbm.fit.final,
cBars = 15,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_sbr = summary_gbm_sbr %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_sbr
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "HUE", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( HUE,(yhat)))+
geom_line()
LIME
library(lime)model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
Prediction
# predict values for test data
pred <- predict(gbm.fit.final,
n.trees = gbm.fit.final$n.trees,
all_data_spread[-train,])
# results
caret::RMSE((pred), all_data_spread[-train,]$sev)## [1] 1.926231CCC(pred, all_data_spread$sev[-train])$rho.c$est## [1] 0.9958621cor(pred, all_data_spread$sev[-train])^2## [1] 0.9921386accuracy_sbr =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_sbr## RMSE r s.shift l.shift C.b CCC CIS
## 1 1.926231 0.9960615 0.9916907 -0.01819114 0.9997998 0.9958621 0.99, 1plot
conc_sbr = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual, predict ))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)## `geom_smooth()` using formula 'y ~ x'Xylella
pics<-list.files("./pics/01-Xylella-tobacco-bg-white")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-Xylella-tobacco-bg-white/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=200, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_Xylella.txt")box = read.table("data/indexes_Xylella.txt")data_xy_load = read_csv("data_pics/01-Xylella-tobacco-severity.csv") %>%
mutate(file = as.character(File)) %>%
dplyr::select(-File)##
## -- Column specification --------------------------------------------------------
## cols(
## File = col_double(),
## ImageJ = col_double(),
## LeafDoctor = col_double(),
## APSAssess = col_double()
## )data_xy = box %>%
separate(leaf, into=c("file","format"), sep =".jpg") %>%
dplyr::select(-format) %>%
full_join(data_xy_load, by="file") %>%
mutate(sev=ImageJ)
data_xy## index mean std Q25 Q50 Q75
## 1 BGI 5.436164e-01 0.060121706 5.121507e-01 5.339519e-01 5.541888e-01
## 2 BI 1.671294e+02 13.092581029 1.590453e+02 1.673032e+02 1.744013e+02
## 3 Blue 1.055687e+02 17.594940645 9.485714e+01 1.037959e+02 1.121020e+02
## 4 GLI 1.386614e-01 0.020247634 1.340011e-01 1.426077e-01 1.493713e-01
## 5 gray 1.815699e+02 13.150761073 1.733458e+02 1.820882e+02 1.895008e+02
## 6 Green 1.933107e+02 13.420773604 1.847347e+02 1.940408e+02 2.020408e+02
## 7 HI 8.683842e-01 0.102006173 8.046512e-01 8.494624e-01 9.244940e-01
## 8 HUE -1.231244e+00 0.938461283 -1.564719e+00 -1.562570e+00 -1.553532e+00
## 9 NGRDI 1.513704e-02 0.011610581 8.698471e-03 1.786423e-02 2.269336e-02
## 10 Red 1.874974e+02 12.336910350 1.802041e+02 1.880408e+02 1.948163e+02
## 11 SCI -1.513704e-02 0.011610581 -2.269336e-02 -1.786423e-02 -8.698471e-03
## 12 SI 2.835890e-01 0.047290707 2.687086e-01 2.891532e-01 3.124621e-01
## 13 VARI 2.097451e-02 0.016012463 1.176282e-02 2.450909e-02 3.152252e-02
## 14 BGI 5.232702e-01 0.064259254 4.901864e-01 5.117849e-01 5.343837e-01
## 15 BI 1.617949e+02 13.149662642 1.538317e+02 1.616828e+02 1.685547e+02
## 16 Blue 9.936791e+01 18.019025309 8.900000e+01 9.702041e+01 1.052194e+02
## 17 GLI 1.489379e-01 0.022855570 1.426525e-01 1.522061e-01 1.609541e-01
## 18 gray 1.763900e+02 13.065001780 1.685468e+02 1.765641e+02 1.836820e+02
## 19 Green 1.888874e+02 13.024434760 1.812449e+02 1.894694e+02 1.965714e+02
## 20 HI 8.288865e-01 0.108060562 7.430341e-01 8.064516e-01 8.758588e-01
## 21 HUE -1.305940e+00 0.838101317 -1.565441e+00 -1.564433e+00 -1.559984e+00
## 22 NGRDI 2.076509e-02 0.013190269 1.435438e-02 2.346119e-02 2.917836e-02
## 23 Red 1.812212e+02 12.792847953 1.736122e+02 1.815612e+02 1.886735e+02
## 24 SCI -2.076509e-02 0.013190269 -2.917836e-02 -2.346119e-02 -1.435438e-02
## 25 SI 2.962883e-01 0.050157294 2.822797e-01 3.044717e-01 3.244522e-01
## 26 VARI 2.834054e-02 0.017835753 1.980149e-02 3.213349e-02 3.987774e-02
## 27 BGI 4.770135e-01 0.082275350 4.382802e-01 4.640944e-01 4.856903e-01
## 28 BI 1.346668e+02 16.903593814 1.247420e+02 1.328121e+02 1.413769e+02
## 29 Blue 7.849852e+01 21.629501426 6.740816e+01 7.530612e+01 8.242857e+01
## 30 GLI 1.841680e-01 0.032885559 1.770874e-01 1.896025e-01 2.013689e-01
## 31 gray 1.483695e+02 16.606670990 1.383943e+02 1.469750e+02 1.557476e+02
## 32 Green 1.625577e+02 15.721708271 1.530357e+02 1.617551e+02 1.703112e+02
## 33 HI 6.317971e-01 0.081442239 5.531797e-01 6.365651e-01 6.761815e-01
## 34 HUE -1.566068e+00 0.002466480 -1.567126e+00 -1.566629e+00 -1.565890e+00
## 35 NGRDI 5.079765e-02 0.014373767 4.323811e-02 5.162627e-02 6.232151e-02
## 36 Red 1.471550e+02 17.493227737 1.365714e+02 1.449184e+02 1.555765e+02
## 37 SCI -5.079765e-02 0.014373767 -6.232151e-02 -5.162627e-02 -4.323811e-02
## 38 SI 3.130982e-01 0.060454942 3.008149e-01 3.208377e-01 3.440621e-01
## 39 VARI 6.729878e-02 0.018001981 5.792041e-02 6.774116e-02 8.341990e-02
## 40 BGI 5.471168e-01 0.064567155 5.118101e-01 5.328164e-01 5.587819e-01
## 41 BI 1.626126e+02 13.429799977 1.551476e+02 1.613388e+02 1.691920e+02
## 42 Blue 1.033741e+02 18.416521184 9.302041e+01 9.967347e+01 1.090000e+02
## 43 GLI 1.380349e-01 0.023116581 1.296631e-01 1.415430e-01 1.522815e-01
## 44 gray 1.765774e+02 13.240406905 1.692520e+02 1.757616e+02 1.836343e+02
## 45 Green 1.879669e+02 13.149076689 1.804898e+02 1.875714e+02 1.952449e+02
## 46 HI 8.632352e-01 0.120741343 7.977528e-01 8.528022e-01 9.247863e-01
## 47 HUE -1.178044e+00 1.005955360 -1.564537e+00 -1.561666e+00 -1.552260e+00
## 48 NGRDI 1.582729e-02 0.014003198 8.299927e-03 1.680000e-02 2.467409e-02
## 49 Red 1.821276e+02 13.014208369 1.748571e+02 1.815306e+02 1.895306e+02
## 50 SCI -1.582729e-02 0.014003198 -2.467409e-02 -1.680000e-02 -8.299927e-03
## 51 SI 2.803030e-01 0.049074718 2.674682e-01 2.903750e-01 3.087919e-01
## 52 VARI 2.191157e-02 0.019195066 1.158766e-02 2.317333e-02 3.360512e-02
## 53 BGI 6.397902e-01 0.057576557 6.033778e-01 6.395914e-01 6.702550e-01
## 54 BI 1.883111e+02 15.966699694 1.786307e+02 1.888041e+02 1.989763e+02
## 55 Blue 1.349047e+02 19.447790916 1.220816e+02 1.347551e+02 1.473673e+02
## 56 GLI 9.997205e-02 0.022113827 8.811560e-02 9.980354e-02 1.142050e-01
## 57 gray 2.013672e+02 16.127278310 1.921515e+02 2.021308e+02 2.119078e+02
## 58 Green 2.101206e+02 16.487520281 2.018980e+02 2.112551e+02 2.204898e+02
## 59 HI 9.952901e-01 0.203793834 9.097048e-01 9.727264e-01 1.007014e+00
## 60 HUE -6.338601e-01 1.346936550 -1.555699e+00 -1.520522e+00 1.359702e+00
## 61 NGRDI 1.230542e-03 0.016915886 -5.605370e-04 2.476373e-03 8.748221e-03
## 62 Red 2.095227e+02 15.755228570 1.998367e+02 2.101735e+02 2.200612e+02
## 63 SCI -1.230542e-03 0.016915886 -8.748221e-03 -2.476373e-03 5.605370e-04
## 64 SI 2.199965e-01 0.041622674 1.968736e-01 2.194891e-01 2.451591e-01
## 65 VARI 1.781766e-03 0.024176415 -8.544055e-04 3.614236e-03 1.267687e-02
## 66 BGI 5.820188e-01 0.067428110 5.315358e-01 5.693426e-01 6.175964e-01
## 67 BI 1.772043e+02 16.401507923 1.661700e+02 1.758584e+02 1.867000e+02
## 68 Blue 1.183991e+02 20.538575261 1.033878e+02 1.151020e+02 1.297755e+02
## 69 GLI 1.248815e-01 0.029034530 1.071104e-01 1.293797e-01 1.484303e-01
## 70 gray 1.914034e+02 16.216391501 1.811177e+02 1.903582e+02 2.006643e+02
## 71 Green 2.024200e+02 15.776128292 1.937755e+02 2.017143e+02 2.111020e+02
## 72 HI 8.999821e-01 0.206456865 7.832061e-01 8.690452e-01 9.447821e-01
## 73 HUE -1.121452e+00 1.066487327 -1.565103e+00 -1.561004e+00 -1.546838e+00
## 74 NGRDI 1.236663e-02 0.019391600 5.042075e-03 1.395027e-02 2.521696e-02
## 75 Red 1.976099e+02 17.276915595 1.853112e+02 1.964898e+02 2.083673e+02
## 76 SCI -1.236663e-02 0.019391600 -2.521696e-02 -1.395027e-02 -5.042075e-03
## 77 SI 2.550979e-01 0.045179189 2.323478e-01 2.627929e-01 2.850668e-01
## 78 VARI 1.709632e-02 0.027170859 7.449380e-03 1.972998e-02 3.474857e-02
## 79 BGI 5.085819e-01 0.065712411 4.773217e-01 4.939795e-01 5.140404e-01
## 80 BI 1.445236e+02 12.891753106 1.374060e+02 1.428794e+02 1.488367e+02
## 81 Blue 8.702637e+01 17.652175236 7.822449e+01 8.355102e+01 8.957143e+01
## 82 GLI 1.571989e-01 0.024133703 1.494502e-01 1.607931e-01 1.712432e-01
## 83 gray 1.579929e+02 12.709072914 1.507976e+02 1.566197e+02 1.630469e+02
## 84 Green 1.700325e+02 12.655048196 1.627347e+02 1.690612e+02 1.759796e+02
## 85 HI 7.936470e-01 0.101355477 7.169132e-01 7.893328e-01 8.598902e-01
## 86 HUE -1.466058e+00 0.523798340 -1.565577e+00 -1.563556e+00 -1.559642e+00
## 87 NGRDI 2.601853e-02 0.013494652 1.692368e-02 2.608425e-02 3.531347e-02
## 88 Red 1.614142e+02 12.201318919 1.543061e+02 1.604082e+02 1.664898e+02
## 89 SCI -2.601853e-02 0.013494652 -3.531347e-02 -2.608425e-02 -1.692368e-02
## 90 SI 3.046540e-01 0.050377029 2.973605e-01 3.144625e-01 3.302657e-01
## 91 VARI 3.514187e-02 0.017979049 2.316398e-02 3.531491e-02 4.770609e-02
## 92 BGI 5.140943e-01 0.066878803 4.825597e-01 5.055288e-01 5.262627e-01
## 93 BI 1.206068e+02 15.753601285 1.125480e+02 1.169682e+02 1.243959e+02
## 94 Blue 7.661937e+01 17.705114033 6.989796e+01 7.322449e+01 7.700000e+01
## 95 GLI 1.916382e-01 0.028509055 1.899393e-01 1.973042e-01 2.040239e-01
## 96 gray 1.331735e+02 15.668948939 1.246965e+02 1.294918e+02 1.377841e+02
## 97 Green 1.480769e+02 14.975951780 1.396122e+02 1.446122e+02 1.533469e+02
## 98 HI 3.599747e-01 0.104268323 2.922038e-01 3.497448e-01 4.250591e-01
## 99 HUE -1.567211e+00 0.001128685 -1.567569e+00 -1.567394e+00 -1.567210e+00
## 100 NGRDI 8.445326e-02 0.017164204 7.931397e-02 8.714782e-02 9.400915e-02
## 101 Red 1.254772e+02 17.404673932 1.156122e+02 1.213878e+02 1.309796e+02
## 102 SCI -8.445326e-02 0.017164204 -9.400915e-02 -8.714782e-02 -7.931397e-02
## 103 SI 2.457563e-01 0.047373487 2.245343e-01 2.455025e-01 2.727635e-01
## 104 VARI 1.166940e-01 0.023432053 1.075013e-01 1.201967e-01 1.304114e-01
## 105 BGI 4.876649e-01 0.075177217 4.522709e-01 4.716773e-01 4.942060e-01
## 106 BI 1.293983e+02 17.900713893 1.187963e+02 1.263242e+02 1.353274e+02
## 107 Blue 7.713667e+01 21.125797394 6.622449e+01 7.298980e+01 7.975510e+01
## 108 GLI 1.826781e-01 0.030965041 1.772256e-01 1.887532e-01 1.986924e-01
## 109 gray 1.425269e+02 17.886501674 1.315521e+02 1.397158e+02 1.494310e+02
## 110 Green 1.563000e+02 17.484522103 1.453061e+02 1.538469e+02 1.641939e+02
## 111 HI 5.989870e-01 0.098148849 5.285039e-01 5.945295e-01 6.481746e-01
## 112 HUE -1.564649e+00 0.051323937 -1.567106e+00 -1.566624e+00 -1.565615e+00
## 113 NGRDI 5.457846e-02 0.016582721 4.544022e-02 5.596591e-02 6.660335e-02
## 114 Red 1.404189e+02 18.544979568 1.289592e+02 1.375306e+02 1.477143e+02
## 115 SCI -5.457846e-02 0.016582721 -6.660335e-02 -5.596591e-02 -4.544022e-02
## 116 SI 2.988513e-01 0.054816385 2.849264e-01 3.075378e-01 3.285267e-01
## 117 VARI 7.304889e-02 0.021377644 6.082658e-02 7.385281e-02 8.893299e-02
## 118 BGI 6.027203e-01 0.058716015 5.723945e-01 5.950962e-01 6.254052e-01
## 119 BI 1.761344e+02 15.816844788 1.683357e+02 1.775403e+02 1.855630e+02
## 120 Blue 1.184159e+02 18.482792674 1.081429e+02 1.177959e+02 1.268980e+02
## 121 GLI 1.017298e-01 0.028620014 8.394042e-02 1.022331e-01 1.220593e-01
## 122 gray 1.886308e+02 16.616136011 1.811637e+02 1.905397e+02 1.987039e+02
## 123 Green 1.958948e+02 18.284371202 1.886327e+02 1.984898e+02 2.073469e+02
## 124 HI 1.154198e+00 0.380323859 8.456439e-01 1.042006e+00 1.386065e+00
## 125 HUE 1.217992e-01 1.545815863 -1.560599e+00 1.536585e+00 1.567414e+00
## 126 NGRDI -1.370210e-02 0.034856111 -3.748638e-02 -4.133512e-03 1.398697e-02
## 127 Red 2.011410e+02 17.022548144 1.914694e+02 2.013265e+02 2.129592e+02
## 128 SCI 1.370210e-02 0.034856111 -1.398697e-02 4.133512e-03 3.748638e-02
## 129 SI 2.620369e-01 0.053865055 2.320727e-01 2.663788e-01 2.978484e-01
## 130 VARI -1.880448e-02 0.048624172 -5.247755e-02 -6.018349e-03 2.065343e-02
## 131 BGI 4.994713e-01 0.062777757 4.713489e-01 4.884036e-01 5.093375e-01
## 132 BI 1.497264e+02 13.170979189 1.423009e+02 1.498734e+02 1.565621e+02
## 133 Blue 8.963874e+01 16.491499847 8.120408e+01 8.761224e+01 9.475510e+01
## 134 GLI 1.691493e-01 0.023713622 1.631395e-01 1.729868e-01 1.810802e-01
## 135 gray 1.643772e+02 13.225405177 1.568870e+02 1.649127e+02 1.717896e+02
## 136 Green 1.785810e+02 12.990664628 1.712857e+02 1.795918e+02 1.863265e+02
## 137 HI 6.927919e-01 0.051306931 6.653543e-01 6.816098e-01 7.152369e-01
## 138 HUE -1.564074e+00 0.078314568 -1.566842e+00 -1.566518e+00 -1.565873e+00
## 139 NGRDI 3.993923e-02 0.008205029 3.532326e-02 4.186875e-02 4.538473e-02
## 140 Red 1.649876e+02 13.655044873 1.569592e+02 1.653673e+02 1.726939e+02
## 141 SCI -3.993923e-02 0.008205029 -4.538473e-02 -4.186875e-02 -3.532326e-02
## 142 SI 3.001807e-01 0.046054075 2.914037e-01 3.078610e-01 3.217996e-01
## 143 VARI 5.372709e-02 0.010172228 4.814663e-02 5.628613e-02 6.046925e-02
## 144 BGI 5.300425e-01 0.068896236 4.929369e-01 5.145007e-01 5.439166e-01
## 145 BI 1.496112e+02 12.531708671 1.424685e+02 1.478781e+02 1.540959e+02
## 146 Blue 9.188030e+01 17.952822086 8.239796e+01 8.789796e+01 9.583673e+01
## 147 GLI 1.381701e-01 0.025542847 1.272303e-01 1.428964e-01 1.547976e-01
## 148 gray 1.624231e+02 12.000969565 1.556090e+02 1.609982e+02 1.670893e+02
## 149 Green 1.722959e+02 11.286002785 1.661429e+02 1.711633e+02 1.766531e+02
## 150 HI 9.428877e-01 0.161228816 8.285921e-01 9.312235e-01 1.035431e+00
## 151 HUE -5.398159e-01 1.437685890 -1.561503e+00 -1.548424e+00 1.529215e+00
## 152 NGRDI 7.252632e-03 0.017531369 -4.178290e-03 8.006498e-03 2.006209e-02
## 153 Red 1.699368e+02 12.869922568 1.616531e+02 1.690408e+02 1.763265e+02
## 154 SCI -7.252632e-03 0.017531369 -2.006209e-02 -8.006498e-03 4.178290e-03
## 155 SI 3.029920e-01 0.052991791 2.897487e-01 3.124898e-01 3.317522e-01
## 156 VARI 9.889932e-03 0.023942800 -5.670858e-03 1.101950e-02 2.742532e-02
## 157 BGI 5.324968e-01 0.069526844 4.994106e-01 5.258065e-01 5.492049e-01
## 158 BI 1.135192e+02 16.596245862 1.057791e+02 1.106585e+02 1.164265e+02
## 159 Blue 7.456771e+01 18.740739102 6.738776e+01 7.153061e+01 7.530612e+01
## 160 GLI 1.866963e-01 0.030547644 1.814737e-01 1.907970e-01 2.004806e-01
## 161 gray 1.250420e+02 16.398548862 1.169126e+02 1.221848e+02 1.288145e+02
## 162 Green 1.388656e+02 15.643941717 1.306531e+02 1.361020e+02 1.435306e+02
## 163 HI 3.183277e-01 0.114943230 2.360965e-01 3.166827e-01 3.961790e-01
## 164 HUE -1.566710e+00 0.001284595 -1.567272e+00 -1.566934e+00 -1.566555e+00
## 165 NGRDI 8.693394e-02 0.018954893 8.092759e-02 8.887890e-02 9.770769e-02
## 166 Red 1.171477e+02 17.962551075 1.080000e+02 1.137959e+02 1.213265e+02
## 167 SCI -8.693394e-02 0.018954893 -9.770769e-02 -8.887890e-02 -8.092759e-02
## 168 SI 2.269589e-01 0.047345603 2.035358e-01 2.266784e-01 2.530682e-01
## 169 VARI 1.218013e-01 0.025982085 1.113648e-01 1.239330e-01 1.378939e-01
## 170 BGI 5.294524e-01 0.070994961 4.970554e-01 5.183500e-01 5.404671e-01
## 171 BI 1.198689e+02 16.040382459 1.119103e+02 1.168980e+02 1.226360e+02
## 172 Blue 7.708734e+01 19.007502082 6.926531e+01 7.340816e+01 7.796939e+01
## 173 GLI 1.738413e-01 0.029554148 1.682256e-01 1.773893e-01 1.880305e-01
## 174 gray 1.316147e+02 15.801279438 1.233271e+02 1.287510e+02 1.350395e+02
## 175 Green 1.443754e+02 15.292952874 1.356735e+02 1.417551e+02 1.486939e+02
## 176 HI 4.930155e-01 0.096269059 4.240023e-01 4.971246e-01 5.559973e-01
## 177 HUE -1.565788e+00 0.002360316 -1.566816e+00 -1.566074e+00 -1.565377e+00
## 178 NGRDI 6.380929e-02 0.016101037 5.486079e-02 6.456954e-02 7.530962e-02
## 179 Red 1.273525e+02 16.644651923 1.187653e+02 1.242245e+02 1.308980e+02
## 180 SCI -6.380929e-02 0.016101037 -7.530962e-02 -6.456954e-02 -5.486079e-02
## 181 SI 2.516587e-01 0.047862102 2.351059e-01 2.563707e-01 2.767461e-01
## 182 VARI 8.832271e-02 0.021400819 7.579152e-02 8.897186e-02 1.041362e-01
## 183 BGI 5.287628e-01 0.070768924 4.966605e-01 5.195187e-01 5.404123e-01
## 184 BI 1.203881e+02 15.185179492 1.135449e+02 1.174843e+02 1.226794e+02
## 185 Blue 7.804833e+01 18.447548041 7.097959e+01 7.475510e+01 7.853061e+01
## 186 GLI 1.822087e-01 0.029944429 1.788503e-01 1.865540e-01 1.953764e-01
## 187 gray 1.325029e+02 14.782043711 1.254898e+02 1.297281e+02 1.354214e+02
## 188 Green 1.464739e+02 13.942594373 1.395510e+02 1.439184e+02 1.499796e+02
## 189 HI 3.929090e-01 0.085102284 3.366584e-01 3.842606e-01 4.423213e-01
## 190 HUE -1.566065e+00 0.045254913 -1.567268e+00 -1.567029e+00 -1.566758e+00
## 191 NGRDI 7.719080e-02 0.015561213 7.316398e-02 8.037772e-02 8.565531e-02
## 192 Red 1.258368e+02 15.997912276 1.178367e+02 1.227347e+02 1.289796e+02
## 193 SCI -7.719080e-02 0.015561213 -8.565531e-02 -8.037772e-02 -7.316398e-02
## 194 SI 2.396596e-01 0.046485414 2.221421e-01 2.419291e-01 2.637740e-01
## 195 VARI 1.073379e-01 0.020453248 1.006270e-01 1.112730e-01 1.192763e-01
## 196 BGI 5.250583e-01 0.071932587 4.942271e-01 5.141938e-01 5.346963e-01
## 197 BI 1.191959e+02 15.958961955 1.109121e+02 1.158714e+02 1.217715e+02
## 198 Blue 7.605282e+01 19.294065268 6.783673e+01 7.234694e+01 7.728571e+01
## 199 GLI 1.740674e-01 0.029238551 1.689298e-01 1.777558e-01 1.873276e-01
## 200 gray 1.308820e+02 15.589922749 1.224805e+02 1.275846e+02 1.339535e+02
## 201 Green 1.434513e+02 14.817460119 1.351224e+02 1.401224e+02 1.468980e+02
## 202 HI 5.108120e-01 0.103492693 4.391941e-01 5.165513e-01 5.820717e-01
## 203 HUE -1.565643e+00 0.002557956 -1.566518e+00 -1.565989e+00 -1.565329e+00
## 204 NGRDI 6.172331e-02 0.016111465 5.240332e-02 6.239724e-02 7.254210e-02
## 205 Red 1.271104e+02 16.608877875 1.180204e+02 1.239184e+02 1.309184e+02
## 206 SCI -6.172331e-02 0.016111465 -7.254210e-02 -6.239724e-02 -5.240332e-02
## 207 SI 2.575596e-01 0.049680653 2.412780e-01 2.635626e-01 2.833271e-01
## 208 VARI 8.516176e-02 0.021582952 7.164497e-02 8.562143e-02 1.001330e-01
## 209 BGI 5.693282e-01 0.056593919 5.373570e-01 5.590145e-01 5.809648e-01
## 210 BI 1.731334e+02 13.744272582 1.642061e+02 1.721689e+02 1.819137e+02
## 211 Blue 1.127348e+02 15.738512027 1.026531e+02 1.108571e+02 1.195102e+02
## 212 GLI 1.254280e-01 0.024619257 1.098302e-01 1.323107e-01 1.432109e-01
## 213 gray 1.871142e+02 13.543546750 1.786573e+02 1.865333e+02 1.959722e+02
## 214 Green 1.975219e+02 12.538211491 1.906939e+02 1.978163e+02 2.055102e+02
## 215 HI 9.367249e-01 0.243034728 7.793290e-01 8.354672e-01 1.043820e+00
## 216 HUE -7.191476e-01 1.382998132 -1.564824e+00 -1.562258e+00 1.539836e+00
## 217 NGRDI 7.404515e-03 0.025480404 -4.434350e-03 1.715658e-02 2.470712e-02
## 218 Red 1.950403e+02 17.959691647 1.831020e+02 1.922245e+02 2.059184e+02
## 219 SCI -7.404515e-03 0.025480404 -2.470712e-02 -1.715658e-02 4.434350e-03
## 220 SI 2.690076e-01 0.046694233 2.535684e-01 2.767424e-01 2.964936e-01
## 221 VARI 1.065781e-02 0.035268911 -6.111649e-03 2.440895e-02 3.453757e-02
## 222 BGI 5.249571e-01 0.062383919 4.972483e-01 5.128702e-01 5.325996e-01
## 223 BI 1.596943e+02 12.559718478 1.548423e+02 1.603918e+02 1.657405e+02
## 224 Blue 9.885602e+01 16.177372461 9.148980e+01 9.722449e+01 1.033878e+02
## 225 GLI 1.535078e-01 0.022791149 1.479623e-01 1.577169e-01 1.643334e-01
## 226 gray 1.744049e+02 12.635562207 1.696160e+02 1.755141e+02 1.809110e+02
## 227 Green 1.876461e+02 12.558593906 1.828163e+02 1.891735e+02 1.945102e+02
## 228 HI 7.633942e-01 0.086782133 7.026002e-01 7.546003e-01 8.001583e-01
## 229 HUE -1.502422e+00 0.425405434 -1.566482e+00 -1.565710e+00 -1.564183e+00
## 230 NGRDI 2.885060e-02 0.011169586 2.353950e-02 3.022025e-02 3.688901e-02
## 231 Red 1.772142e+02 13.124324413 1.724694e+02 1.781837e+02 1.842653e+02
## 232 SCI -2.885060e-02 0.011169586 -3.688901e-02 -3.022025e-02 -2.353950e-02
## 233 SI 2.873387e-01 0.046363354 2.775557e-01 2.957897e-01 3.104482e-01
## 234 VARI 3.941850e-02 0.014949344 3.248707e-02 4.090942e-02 5.043292e-02
## 235 BGI 5.450818e-01 0.071943333 5.070647e-01 5.309679e-01 5.598492e-01
## 236 BI 1.537413e+02 14.514226460 1.452709e+02 1.527578e+02 1.612965e+02
## 237 Blue 9.624747e+01 19.376548376 8.561224e+01 9.309184e+01 1.020816e+02
## 238 GLI 1.291434e-01 0.026067265 1.168399e-01 1.314071e-01 1.455899e-01
## 239 gray 1.663356e+02 14.218634914 1.581210e+02 1.656253e+02 1.743097e+02
## 240 Green 1.753847e+02 13.699549873 1.675102e+02 1.749388e+02 1.832041e+02
## 241 HI 9.989140e-01 0.154262450 9.007172e-01 9.970515e-01 1.107483e+00
## 242 HUE -1.918447e-02 1.533749487 -1.555708e+00 -1.059374e+00 1.556977e+00
## 243 NGRDI 6.146476e-04 0.017263368 -1.190220e-02 3.111587e-04 1.172595e-02
## 244 Red 1.752928e+02 15.070329664 1.658776e+02 1.751633e+02 1.850000e+02
## 245 SCI -6.146476e-04 0.017263368 -1.172595e-02 -3.111587e-04 1.190220e-02
## 246 SI 2.964377e-01 0.054446703 2.857464e-01 3.082798e-01 3.249796e-01
## 247 VARI 8.426975e-04 0.023651500 -1.632448e-02 4.306633e-04 1.596546e-02
## 248 BGI 5.233619e-01 0.067891340 4.894052e-01 5.096004e-01 5.332910e-01
## 249 BI 1.474502e+02 14.535469934 1.384085e+02 1.459328e+02 1.537968e+02
## 250 Blue 8.954300e+01 19.216559334 7.900000e+01 8.600000e+01 9.425000e+01
## 251 GLI 1.384899e-01 0.021684567 1.320047e-01 1.417562e-01 1.504612e-01
## 252 gray 1.601093e+02 14.665721191 1.507577e+02 1.587924e+02 1.671115e+02
## 253 Green 1.697510e+02 15.108230617 1.599592e+02 1.685306e+02 1.775510e+02
## 254 HI 9.600963e-01 0.117854449 8.875060e-01 9.615579e-01 1.041944e+00
## 255 HUE -3.763524e-01 1.476921598 -1.557815e+00 -1.530458e+00 1.532104e+00
## 256 NGRDI 4.506612e-03 0.013642025 -4.956473e-03 4.568824e-03 1.346833e-02
## 257 Red 1.680855e+02 13.224252460 1.596939e+02 1.674286e+02 1.750408e+02
## 258 SCI -4.506612e-03 0.013642025 -1.346833e-02 -4.568824e-03 4.956473e-03
## 259 SI 3.110701e-01 0.055923605 2.990188e-01 3.218473e-01 3.405766e-01
## 260 VARI 6.327836e-03 0.018573545 -6.720936e-03 6.251881e-03 1.827972e-02
## 261 BGI 5.046310e-01 0.074173138 4.675413e-01 4.885906e-01 5.146986e-01
## 262 BI 1.304131e+02 14.578500953 1.228730e+02 1.277702e+02 1.335876e+02
## 263 Blue 7.989120e+01 19.065019246 7.089796e+01 7.606122e+01 8.185714e+01
## 264 GLI 1.767202e-01 0.029184161 1.715935e-01 1.826426e-01 1.915274e-01
## 265 gray 1.434144e+02 14.204844472 1.357792e+02 1.410068e+02 1.472919e+02
## 266 Green 1.570034e+02 13.600259390 1.492755e+02 1.548980e+02 1.617347e+02
## 267 HI 5.833598e-01 0.059904895 5.446780e-01 5.815673e-01 6.262376e-01
## 268 HUE -1.566145e+00 0.001859905 -1.566971e+00 -1.566438e+00 -1.565851e+00
## 269 NGRDI 5.448390e-02 0.011367371 4.934291e-02 5.553927e-02 6.151109e-02
## 270 Red 1.409558e+02 14.516077053 1.334694e+02 1.384694e+02 1.446122e+02
## 271 SCI -5.448390e-02 0.011367371 -6.151109e-02 -5.553927e-02 -4.934291e-02
## 272 SI 2.830816e-01 0.052495161 2.686012e-01 2.924937e-01 3.122760e-01
## 273 VARI 7.379653e-02 0.014168969 6.635839e-02 7.524178e-02 8.276499e-02
## 274 BGI 5.484175e-01 0.070442358 5.122256e-01 5.371213e-01 5.601112e-01
## 275 BI 1.533915e+02 13.434078537 1.453086e+02 1.530967e+02 1.609798e+02
## 276 Blue 9.704841e+01 18.584819973 8.663776e+01 9.446939e+01 1.031224e+02
## 277 GLI 1.322866e-01 0.023462062 1.251843e-01 1.342537e-01 1.456666e-01
## 278 gray 1.661886e+02 13.226740967 1.581150e+02 1.663080e+02 1.742349e+02
## 279 Green 1.759391e+02 13.073849723 1.679439e+02 1.763061e+02 1.841429e+02
## 280 HI 9.349741e-01 0.109724640 8.743212e-01 9.328606e-01 9.960062e-01
## 281 HUE -8.118095e-01 1.290596417 -1.558512e+00 -1.547926e+00 -1.198032e+00
## 282 NGRDI 7.278826e-03 0.012102216 4.490535e-04 7.498980e-03 1.418686e-02
## 283 Red 1.734073e+02 12.994255287 1.656531e+02 1.738571e+02 1.819388e+02
## 284 SCI -7.278826e-03 0.012102216 -1.418686e-02 -7.498980e-03 -4.490535e-04
## 285 SI 2.873982e-01 0.053222932 2.765324e-01 2.968311e-01 3.157077e-01
## 286 VARI 1.009202e-02 0.016641445 6.187760e-04 1.041315e-02 1.960147e-02
## 287 BGI 5.207860e-01 0.071626850 4.865130e-01 5.107144e-01 5.329111e-01
## 288 BI 1.395525e+02 17.068653186 1.296181e+02 1.374920e+02 1.464138e+02
## 289 Blue 8.588464e+01 20.919796122 7.487245e+01 8.273469e+01 9.046939e+01
## 290 GLI 1.510602e-01 0.024898958 1.441184e-01 1.536548e-01 1.641193e-01
## 291 gray 1.521675e+02 17.227046719 1.419040e+02 1.503289e+02 1.599090e+02
## 292 Green 1.631570e+02 17.484097310 1.526888e+02 1.615204e+02 1.716531e+02
## 293 HI 8.131070e-01 0.109803057 7.391249e-01 8.103076e-01 8.779259e-01
## 294 HUE -1.437451e+00 0.585529535 -1.564366e+00 -1.561828e+00 -1.556131e+00
## 295 NGRDI 2.267746e-02 0.013105007 1.455317e-02 2.303205e-02 3.144623e-02
## 296 Red 1.558643e+02 16.289147783 1.463061e+02 1.538980e+02 1.632296e+02
## 297 SCI -2.267746e-02 0.013105007 -3.144623e-02 -2.303205e-02 -1.455317e-02
## 298 SI 2.972665e-01 0.056629525 2.825668e-01 3.029235e-01 3.255470e-01
## 299 VARI 3.093311e-02 0.017741945 1.989417e-02 3.141559e-02 4.288234e-02
## 300 BGI 5.862999e-01 0.059612981 5.543382e-01 5.791280e-01 6.022413e-01
## 301 BI 1.681839e+02 16.083602113 1.582333e+02 1.692416e+02 1.782315e+02
## 302 Blue 1.105194e+02 19.460263483 9.816327e+01 1.098163e+02 1.197857e+02
## 303 GLI 1.055336e-01 0.019042350 9.568728e-02 1.073600e-01 1.184691e-01
## 304 gray 1.804409e+02 16.688221981 1.701113e+02 1.819661e+02 1.913046e+02
## 305 Green 1.875037e+02 17.856387955 1.763776e+02 1.892041e+02 1.995510e+02
## 306 HI 1.155520e+00 0.186526044 1.016261e+00 1.171134e+00 1.281588e+00
## 307 HUE 8.590737e-01 1.280279638 1.479247e+00 1.562279e+00 1.565508e+00
## 308 NGRDI -1.585161e-02 0.018803599 -2.912053e-02 -1.746792e-02 -1.527957e-03
## 309 Red 1.932343e+02 14.976397222 1.844082e+02 1.944286e+02 2.035510e+02
## 310 SCI 1.585161e-02 0.018803599 1.527957e-03 1.746792e-02 2.912053e-02
## 311 SI 2.769417e-01 0.050696385 2.576928e-01 2.808089e-01 3.070842e-01
## 312 VARI -2.186420e-02 0.026067428 -4.019747e-02 -2.447727e-02 -2.192770e-03
## 313 BGI 5.192105e-01 0.077532642 4.832943e-01 5.051208e-01 5.279565e-01
## 314 BI 1.238033e+02 17.501235064 1.147951e+02 1.217751e+02 1.293339e+02
## 315 Blue 7.798505e+01 21.099634579 6.795408e+01 7.457143e+01 8.144898e+01
## 316 GLI 1.722109e-01 0.030406559 1.675139e-01 1.774346e-01 1.868156e-01
## 317 gray 1.358587e+02 17.297326137 1.266558e+02 1.341108e+02 1.421687e+02
## 318 Green 1.484667e+02 16.885049723 1.391020e+02 1.471429e+02 1.556990e+02
## 319 HI 5.641178e-01 0.083052966 5.081380e-01 5.643149e-01 6.267648e-01
## 320 HUE -1.565462e+00 0.002165983 -1.566662e+00 -1.565954e+00 -1.564926e+00
## 321 NGRDI 5.525277e-02 0.013887055 4.730644e-02 5.612322e-02 6.416064e-02
## 322 Red 1.331720e+02 17.650637197 1.240969e+02 1.314082e+02 1.392041e+02
## 323 SCI -5.525277e-02 0.013887055 -6.416064e-02 -5.612322e-02 -4.730644e-02
## 324 SI 2.693215e-01 0.053556320 2.545540e-01 2.765221e-01 2.984969e-01
## 325 VARI 7.563644e-02 0.018043312 6.424849e-02 7.658353e-02 8.744597e-02
## 326 BGI 5.297517e-01 0.089256861 4.888340e-01 5.099803e-01 5.341361e-01
## 327 BI 1.216734e+02 18.012412608 1.126482e+02 1.193315e+02 1.260506e+02
## 328 Blue 7.863188e+01 22.388229120 6.810204e+01 7.437755e+01 8.048469e+01
## 329 GLI 1.761516e-01 0.037100282 1.725122e-01 1.839830e-01 1.946178e-01
## 330 gray 1.335686e+02 17.555069909 1.245575e+02 1.317249e+02 1.387731e+02
## 331 Green 1.467441e+02 16.879598346 1.378980e+02 1.454898e+02 1.532041e+02
## 332 HI 4.695773e-01 0.078643417 4.138743e-01 4.563732e-01 5.141532e-01
## 333 HUE -1.563679e+00 0.075712046 -1.567170e+00 -1.566671e+00 -1.565895e+00
## 334 NGRDI 6.704229e-02 0.017179989 6.129031e-02 7.113344e-02 7.797700e-02
## 335 Red 1.286482e+02 18.256359011 1.196786e+02 1.265102e+02 1.332041e+02
## 336 SCI -6.704229e-02 0.017179989 -7.797700e-02 -7.113344e-02 -6.129031e-02
## 337 SI 2.500272e-01 0.055117424 2.396514e-01 2.602473e-01 2.776815e-01
## 338 VARI 9.240721e-02 0.021812318 8.464712e-02 9.768407e-02 1.070202e-01
## 339 BGI 5.237393e-01 0.070387743 4.916762e-01 5.131221e-01 5.342015e-01
## 340 BI 1.317087e+02 16.350242202 1.224276e+02 1.291465e+02 1.370149e+02
## 341 Blue 8.201910e+01 19.717060635 7.220408e+01 7.869388e+01 8.548980e+01
## 342 GLI 1.566632e-01 0.026455511 1.498524e-01 1.603016e-01 1.703397e-01
## 343 gray 1.438773e+02 16.275324712 1.343790e+02 1.413986e+02 1.496976e+02
## 344 Green 1.551395e+02 16.041928337 1.455102e+02 1.526122e+02 1.615102e+02
## 345 HI 7.323317e-01 0.119954555 6.488902e-01 7.256163e-01 8.124658e-01
## 346 HUE -1.509422e+00 0.385425590 -1.565185e+00 -1.563745e+00 -1.560412e+00
## 347 NGRDI 3.308341e-02 0.015572896 2.241998e-02 3.398126e-02 4.385281e-02
## 348 Red 1.453519e+02 16.578655801 1.355816e+02 1.432653e+02 1.517143e+02
## 349 SCI -3.308341e-02 0.015572896 -4.385281e-02 -3.398126e-02 -2.241998e-02
## 350 SI 2.850766e-01 0.052848437 2.707255e-01 2.926301e-01 3.129353e-01
## 351 VARI 4.523116e-02 0.021233134 3.039835e-02 4.646386e-02 5.972904e-02
## 352 BGI 5.220014e-01 0.069163883 4.854602e-01 5.079474e-01 5.379613e-01
## 353 BI 1.314475e+02 16.742216580 1.216378e+02 1.281832e+02 1.364430e+02
## 354 Blue 8.200062e+01 19.682137241 7.155612e+01 7.789796e+01 8.516327e+01
## 355 GLI 1.609071e-01 0.026412282 1.543688e-01 1.655955e-01 1.752237e-01
## 356 gray 1.437987e+02 16.683778915 1.337721e+02 1.407774e+02 1.494348e+02
## 357 Green 1.556073e+02 16.278291231 1.455969e+02 1.529592e+02 1.618571e+02
## 358 HI 6.893229e-01 0.094072275 6.356398e-01 6.773564e-01 7.316739e-01
## 359 HUE -1.530661e+00 0.314714694 -1.565630e+00 -1.564763e+00 -1.563580e+00
## 360 NGRDI 3.886822e-02 0.012793738 3.300436e-02 4.077430e-02 4.665043e-02
## 361 Red 1.441778e+02 17.289151362 1.337755e+02 1.407959e+02 1.500612e+02
## 362 SCI -3.886822e-02 0.012793738 -4.665043e-02 -4.077430e-02 -3.300436e-02
## 363 SI 2.812932e-01 0.050396682 2.641227e-01 2.898611e-01 3.107013e-01
## 364 VARI 5.307316e-02 0.017197156 4.510258e-02 5.548978e-02 6.320527e-02
## 365 BGI 5.238761e-01 0.064520931 4.887377e-01 5.123290e-01 5.402303e-01
## 366 BI 1.225945e+02 15.852749019 1.136842e+02 1.200678e+02 1.273741e+02
## 367 Blue 7.836941e+01 17.726274608 7.016327e+01 7.513265e+01 8.082143e+01
## 368 GLI 1.793160e-01 0.027298440 1.729080e-01 1.840040e-01 1.934194e-01
## 369 gray 1.348778e+02 15.820359143 1.255633e+02 1.324440e+02 1.403712e+02
## 370 Green 1.485640e+02 15.295992026 1.391020e+02 1.463265e+02 1.546735e+02
## 371 HI 4.534645e-01 0.109331974 3.731497e-01 4.491759e-01 5.323732e-01
## 372 HUE -1.566529e+00 0.001371604 -1.567189e+00 -1.566817e+00 -1.566281e+00
## 373 NGRDI 6.986914e-02 0.017007630 6.062804e-02 7.182358e-02 8.085661e-02
## 374 Red 1.295541e+02 17.225032577 1.194490e+02 1.268571e+02 1.360612e+02
## 375 SCI -6.986914e-02 0.017007630 -8.085661e-02 -7.182358e-02 -6.062804e-02
## 376 SI 2.504249e-01 0.045659934 2.293685e-01 2.541229e-01 2.796171e-01
## 377 VARI 9.673872e-02 0.023168280 8.277490e-02 9.924512e-02 1.125975e-01
## 378 BGI 5.354317e-01 0.071313996 4.986688e-01 5.225076e-01 5.487635e-01
## 379 BI 1.273232e+02 17.883226072 1.171775e+02 1.238624e+02 1.323961e+02
## 380 Blue 8.088840e+01 20.953698464 7.004082e+01 7.636735e+01 8.428571e+01
## 381 GLI 1.521554e-01 0.028233884 1.416277e-01 1.570313e-01 1.685301e-01
## 382 gray 1.388224e+02 17.808564274 1.285083e+02 1.355726e+02 1.444364e+02
## 383 Green 1.493427e+02 17.554683179 1.389184e+02 1.463061e+02 1.550816e+02
## 384 HI 7.364305e-01 0.132900042 6.589940e-01 7.132988e-01 8.114374e-01
## 385 HUE -1.382983e+00 0.708095175 -1.564830e+00 -1.563338e+00 -1.558951e+00
## 386 NGRDI 3.202228e-02 0.017438497 2.129780e-02 3.493512e-02 4.281298e-02
## 387 Red 1.402576e+02 18.095510463 1.294490e+02 1.371633e+02 1.471020e+02
## 388 SCI -3.202228e-02 0.017438497 -4.281298e-02 -3.493512e-02 -2.129780e-02
## 389 SI 2.759451e-01 0.051104317 2.621901e-01 2.830875e-01 3.049807e-01
## 390 VARI 4.400084e-02 0.023548247 2.953804e-02 4.806749e-02 5.846652e-02
## 391 BGI 4.965653e-01 0.079627745 4.576112e-01 4.790190e-01 5.062927e-01
## 392 BI 1.405538e+02 18.275841129 1.291275e+02 1.373098e+02 1.480468e+02
## 393 Blue 8.387305e+01 21.866658446 7.244898e+01 7.885714e+01 8.744898e+01
## 394 GLI 1.689130e-01 0.033347164 1.558015e-01 1.762004e-01 1.902043e-01
## 395 gray 1.540842e+02 18.102754957 1.427699e+02 1.510731e+02 1.620069e+02
## 396 Green 1.670471e+02 17.118456525 1.567092e+02 1.642449e+02 1.745918e+02
## 397 HI 7.176766e-01 0.156018401 6.036244e-01 6.936921e-01 8.038587e-01
## 398 HUE -1.386300e+00 0.715231681 -1.566762e+00 -1.565691e+00 -1.563144e+00
## 399 NGRDI 3.762537e-02 0.021819521 2.309526e-02 4.044680e-02 5.397942e-02
## 400 Red 1.554050e+02 20.171144561 1.412857e+02 1.523776e+02 1.666173e+02
## 401 SCI -3.762537e-02 0.021819521 -5.397942e-02 -4.044680e-02 -2.309526e-02
## 402 SI 3.063417e-01 0.059244516 2.909569e-01 3.187842e-01 3.387896e-01
## 403 VARI 5.033993e-02 0.028821026 3.174379e-02 5.402534e-02 7.145717e-02
## 404 BGI 5.211453e-01 0.069683216 4.899566e-01 5.078216e-01 5.281845e-01
## 405 BI 1.427829e+02 14.179526571 1.353625e+02 1.418720e+02 1.480226e+02
## 406 Blue 8.872163e+01 18.387599503 8.004082e+01 8.600000e+01 9.218367e+01
## 407 GLI 1.610264e-01 0.027185917 1.550044e-01 1.656042e-01 1.756278e-01
## 408 gray 1.562468e+02 13.884853956 1.487423e+02 1.557257e+02 1.620402e+02
## 409 Green 1.691127e+02 13.284698595 1.617092e+02 1.689490e+02 1.754082e+02
## 410 HI 6.927941e-01 0.097553236 6.341386e-01 6.812087e-01 7.398326e-01
## 411 HUE -1.533661e+00 0.302308024 -1.566560e+00 -1.565762e+00 -1.564461e+00
## 412 NGRDI 3.856387e-02 0.013762036 3.097191e-02 4.032369e-02 4.751631e-02
## 413 Red 1.567337e+02 14.609785540 1.484286e+02 1.560510e+02 1.632245e+02
## 414 SCI -3.856387e-02 0.013762036 -4.751631e-02 -4.032369e-02 -3.097191e-02
## 415 SI 2.823817e-01 0.049759910 2.722940e-01 2.911007e-01 3.073090e-01
## 416 VARI 5.256782e-02 0.018302823 4.258658e-02 5.482058e-02 6.426120e-02
## 417 BGI 5.554579e-01 0.070899774 5.164319e-01 5.414773e-01 5.696930e-01
## 418 BI 1.625161e+02 13.595106263 1.553504e+02 1.619489e+02 1.690269e+02
## 419 Blue 1.021844e+02 19.439602379 9.126531e+01 9.897959e+01 1.082653e+02
## 420 GLI 1.173093e-01 0.024944652 1.026677e-01 1.201435e-01 1.340897e-01
## 421 gray 1.750791e+02 13.771141478 1.678362e+02 1.749311e+02 1.823608e+02
## 422 Green 1.829619e+02 14.707116395 1.747143e+02 1.830612e+02 1.918163e+02
## 423 HI 1.115773e+00 0.221058421 9.355420e-01 1.069990e+00 1.256200e+00
## 424 HUE 4.073203e-01 1.486136146 -1.548492e+00 1.549868e+00 1.565141e+00
## 425 NGRDI -1.242718e-02 0.024077290 -2.765778e-02 -7.236320e-03 6.693440e-03
## 426 Red 1.873961e+02 12.679792582 1.814082e+02 1.877347e+02 1.941429e+02
## 427 SCI 1.242718e-02 0.024077290 -6.693440e-03 7.236320e-03 2.765778e-02
## 428 SI 2.993312e-01 0.060193384 2.797104e-01 3.094472e-01 3.342742e-01
## 429 VARI -1.665706e-02 0.032480951 -3.780662e-02 -1.023866e-02 9.349649e-03
## 430 BGI 5.400814e-01 0.071925536 5.025206e-01 5.282392e-01 5.562977e-01
## 431 BI 1.166009e+02 17.649485240 1.074675e+02 1.127090e+02 1.206427e+02
## 432 Blue 7.733526e+01 19.813312323 6.912245e+01 7.300000e+01 7.744898e+01
## 433 GLI 1.819086e-01 0.031842210 1.784304e-01 1.885623e-01 1.972451e-01
## 434 gray 1.281973e+02 17.401674800 1.187700e+02 1.244686e+02 1.332056e+02
## 435 Green 1.418990e+02 16.402334762 1.326735e+02 1.385306e+02 1.474490e+02
## 436 HI 3.358040e-01 0.141950025 2.295720e-01 3.342149e-01 4.326241e-01
## 437 HUE -1.566216e+00 0.034081696 -1.567239e+00 -1.566956e+00 -1.566581e+00
## 438 NGRDI 8.332864e-02 0.021703746 7.463147e-02 8.632743e-02 9.736409e-02
## 439 Red 1.206901e+02 19.444305373 1.095102e+02 1.164490e+02 1.271429e+02
## 440 SCI -8.332864e-02 0.021703746 -9.736409e-02 -8.632743e-02 -7.463147e-02
## 441 SI 2.237954e-01 0.049262601 1.963739e-01 2.257212e-01 2.552688e-01
## 442 VARI 1.172587e-01 0.030378973 1.027964e-01 1.203482e-01 1.378702e-01
## 443 BGI 5.346590e-01 0.072480016 4.939433e-01 5.217933e-01 5.500966e-01
## 444 BI 1.586044e+02 15.232760193 1.480257e+02 1.592621e+02 1.674833e+02
## 445 Blue 9.974399e+01 19.625582486 8.767347e+01 9.753061e+01 1.065306e+02
## 446 GLI 1.492086e-01 0.030265053 1.345262e-01 1.519202e-01 1.694415e-01
## 447 gray 1.728196e+02 14.910294112 1.625260e+02 1.740088e+02 1.820270e+02
## 448 Green 1.853867e+02 13.983644221 1.761429e+02 1.864490e+02 1.939796e+02
## 449 HI 7.831766e-01 0.157492115 6.635638e-01 7.496516e-01 8.944681e-01
## 450 HUE -1.195512e+00 0.992478235 -1.566905e+00 -1.565174e+00 -1.557669e+00
## 451 NGRDI 2.685379e-02 0.020567838 1.169958e-02 2.797829e-02 4.233799e-02
## 452 Red 1.760092e+02 16.887354429 1.637551e+02 1.780816e+02 1.872653e+02
## 453 SCI -2.685379e-02 0.020567838 -4.233799e-02 -2.797829e-02 -1.169958e-02
## 454 SI 2.815520e-01 0.052593393 2.700558e-01 2.900691e-01 3.096276e-01
## 455 VARI 3.671980e-02 0.027712050 1.633129e-02 3.888313e-02 5.778265e-02
## 456 BGI 5.329802e-01 0.068257145 4.972797e-01 5.179919e-01 5.447359e-01
## 457 BI 1.635488e+02 12.945816619 1.563341e+02 1.626355e+02 1.691450e+02
## 458 Blue 1.015314e+02 18.453136243 9.114286e+01 9.816327e+01 1.066735e+02
## 459 GLI 1.421414e-01 0.024387848 1.354700e-01 1.466366e-01 1.556862e-01
## 460 gray 1.778464e+02 12.679172879 1.707905e+02 1.773190e+02 1.839119e+02
## 461 Green 1.895340e+02 12.360020850 1.827551e+02 1.893469e+02 1.959796e+02
## 462 HI 8.747204e-01 0.131212043 7.900273e-01 8.588413e-01 9.270067e-01
## 463 HUE -1.168346e+00 1.016582098 -1.564745e+00 -1.561492e+00 -1.552287e+00
## 464 NGRDI 1.498786e-02 0.014486335 8.352107e-03 1.641707e-02 2.505604e-02
## 465 Red 1.839979e+02 12.930806008 1.768367e+02 1.834286e+02 1.907908e+02
## 466 SCI -1.498786e-02 0.014486335 -2.505604e-02 -1.641707e-02 -8.352107e-03
## 467 SI 2.934039e-01 0.052761594 2.782379e-01 3.035117e-01 3.241928e-01
## 468 VARI 2.054019e-02 0.019810177 1.169519e-02 2.246015e-02 3.427607e-02
## 469 BGI 4.952852e-01 0.074527453 4.633113e-01 4.770828e-01 4.955592e-01
## 470 BI 1.483821e+02 13.641513684 1.411250e+02 1.470247e+02 1.536048e+02
## 471 Blue 8.830111e+01 19.299668071 7.940816e+01 8.428571e+01 9.048469e+01
## 472 GLI 1.708486e-01 0.028348125 1.664811e-01 1.775059e-01 1.849260e-01
## 473 gray 1.629022e+02 13.177974186 1.557700e+02 1.620764e+02 1.687101e+02
## 474 Green 1.770550e+02 12.436010126 1.703316e+02 1.767755e+02 1.831429e+02
## 475 HI 6.954710e-01 0.075476999 6.471875e-01 6.810771e-01 7.264981e-01
## 476 HUE -1.554096e+00 0.184547448 -1.567000e+00 -1.566607e+00 -1.565683e+00
## 477 NGRDI 4.007713e-02 0.011720850 3.398657e-02 4.252720e-02 4.800629e-02
## 478 Red 1.635605e+02 13.610611750 1.558367e+02 1.626531e+02 1.703469e+02
## 479 SCI -4.007713e-02 0.011720850 -4.800629e-02 -4.252720e-02 -3.398657e-02
## 480 SI 3.047971e-01 0.054012704 3.008835e-01 3.168290e-01 3.300526e-01
## 481 VARI 5.365908e-02 0.014958927 4.605950e-02 5.680146e-02 6.384184e-02
## 482 BGI 5.442703e-01 0.061071971 5.108391e-01 5.316004e-01 5.582811e-01
## 483 BI 1.716234e+02 14.241336350 1.631903e+02 1.709974e+02 1.790346e+02
## 484 Blue 1.089607e+02 17.636281895 9.822449e+01 1.065102e+02 1.161020e+02
## 485 GLI 1.421834e-01 0.021910846 1.347103e-01 1.470360e-01 1.555523e-01
## 486 gray 1.866801e+02 14.352778874 1.783008e+02 1.863492e+02 1.944195e+02
## 487 Green 1.994104e+02 14.152619315 1.912653e+02 1.997143e+02 2.073878e+02
## 488 HI 8.212868e-01 0.070612097 7.768051e-01 8.064516e-01 8.744676e-01
## 489 HUE -1.538299e+00 0.264743541 -1.565584e+00 -1.564448e+00 -1.561154e+00
## 490 NGRDI 2.093508e-02 0.008721118 1.436158e-02 2.239640e-02 2.677305e-02
## 491 Red 1.913201e+02 14.850767867 1.826939e+02 1.904796e+02 1.994898e+02
## 492 SCI -2.093508e-02 0.008721118 -2.677305e-02 -2.239640e-02 -1.436158e-02
## 493 SI 2.778811e-01 0.044888872 2.657416e-01 2.845454e-01 3.020852e-01
## 494 VARI 2.884547e-02 0.011799911 1.994195e-02 3.088715e-02 3.656663e-02
## 495 BGI 5.609856e-01 0.083853109 5.083240e-01 5.498266e-01 5.932783e-01
## 496 BI 1.670620e+02 17.653470060 1.547940e+02 1.676717e+02 1.777593e+02
## 497 Blue 1.075657e+02 23.939310334 9.142857e+01 1.054082e+02 1.186122e+02
## 498 GLI 1.253213e-01 0.029575411 1.108515e-01 1.287683e-01 1.441070e-01
## 499 gray 1.804022e+02 17.343059987 1.685135e+02 1.814482e+02 1.917514e+02
## 500 Green 1.900296e+02 16.765897302 1.789388e+02 1.913469e+02 2.014286e+02
## 501 HI 9.881746e-01 0.136377215 8.972003e-01 9.842738e-01 1.071831e+00
## 502 HUE -1.310478e-01 1.526569539 -1.558046e+00 -1.485676e+00 1.551483e+00
## 503 NGRDI 2.175897e-03 0.014219689 -7.472696e-03 1.663536e-03 1.156175e-02
## 504 Red 1.892721e+02 17.445625372 1.773469e+02 1.901020e+02 2.008571e+02
## 505 SCI -2.175897e-03 0.014219689 -1.156175e-02 -1.663536e-03 7.472696e-03
## 506 SI 2.828440e-01 0.063074857 2.559740e-01 2.878431e-01 3.218741e-01
## 507 VARI 2.831771e-03 0.019719296 -1.061048e-02 2.336085e-03 1.601880e-02
## 508 BGI 4.907328e-01 0.082737798 4.463164e-01 4.736623e-01 5.029475e-01
## 509 BI 1.258034e+02 17.748456556 1.152794e+02 1.224017e+02 1.316392e+02
## 510 Blue 7.546188e+01 21.803555833 6.334694e+01 7.081633e+01 7.971429e+01
## 511 GLI 1.814295e-01 0.035422105 1.730819e-01 1.888755e-01 2.022825e-01
## 512 gray 1.384293e+02 17.366016781 1.278518e+02 1.353896e+02 1.449447e+02
## 513 Green 1.516203e+02 16.236901794 1.413265e+02 1.493469e+02 1.586735e+02
## 514 HI 6.031791e-01 0.133998693 5.242137e-01 5.767701e-01 6.542321e-01
## 515 HUE -1.513842e+00 0.393318934 -1.566849e+00 -1.566225e+00 -1.565233e+00
## 516 NGRDI 5.401157e-02 0.020239338 4.432275e-02 5.768847e-02 6.762455e-02
## 517 Red 1.365403e+02 18.948153899 1.247347e+02 1.330000e+02 1.434286e+02
## 518 SCI -5.401157e-02 0.020239338 -6.762455e-02 -5.768847e-02 -4.432275e-02
## 519 SI 2.973053e-01 0.059220510 2.826931e-01 3.059369e-01 3.296189e-01
## 520 VARI 7.225054e-02 0.026248824 5.963046e-02 7.719531e-02 8.968269e-02
## 521 BGI 5.651627e-01 0.070270315 5.274869e-01 5.538134e-01 5.826988e-01
## 522 BI 1.670819e+02 15.641189025 1.580787e+02 1.673641e+02 1.760445e+02
## 523 Blue 1.076521e+02 20.462167790 9.581633e+01 1.058367e+02 1.157143e+02
## 524 GLI 1.207909e-01 0.022726178 1.122594e-01 1.245610e-01 1.342208e-01
## 525 gray 1.802434e+02 15.751071332 1.714407e+02 1.807724e+02 1.899738e+02
## 526 Green 1.893013e+02 16.011764860 1.803878e+02 1.900204e+02 1.997755e+02
## 527 HI 1.022842e+00 0.144981807 9.306627e-01 1.003411e+00 1.127672e+00
## 528 HUE 6.781276e-02 1.515971846 -1.552779e+00 1.209506e+00 1.559581e+00
## 529 NGRDI -2.392550e-03 0.015107182 -1.358419e-02 -3.861856e-04 8.088395e-03
## 530 Red 1.901378e+02 15.217866158 1.816122e+02 1.912245e+02 1.996122e+02
## 531 SCI 2.392550e-03 0.015107182 -8.088395e-03 3.861856e-04 1.358419e-02
## 532 SI 2.823076e-01 0.055760004 2.632192e-01 2.889896e-01 3.141424e-01
## 533 VARI -3.142441e-03 0.020961349 -1.872552e-02 -5.305826e-04 1.108096e-02
## 534 BGI 5.439335e-01 0.075919415 5.029933e-01 5.276799e-01 5.597873e-01
## 535 BI 1.440937e+02 17.319382774 1.331346e+02 1.421219e+02 1.508383e+02
## 536 Blue 9.128441e+01 22.083875611 7.859184e+01 8.683673e+01 9.681633e+01
## 537 GLI 1.369913e-01 0.024998268 1.291644e-01 1.409693e-01 1.518008e-01
## 538 gray 1.562958e+02 17.194593334 1.453090e+02 1.546045e+02 1.635488e+02
## 539 Green 1.660256e+02 17.041478112 1.550612e+02 1.645306e+02 1.734898e+02
## 540 HI 8.913054e-01 0.108882529 8.199500e-01 8.882682e-01 9.541194e-01
## 541 HUE -1.120553e+00 1.048817281 -1.561080e+00 -1.555048e+00 -1.532264e+00
## 542 NGRDI 1.233694e-02 0.011861531 5.203341e-03 1.300793e-02 2.096676e-02
## 543 Red 1.619811e+02 16.601483266 1.513265e+02 1.609592e+02 1.696735e+02
## 544 SCI -1.233694e-02 0.011861531 -2.096676e-02 -1.300793e-02 -5.203341e-03
## 545 SI 2.869752e-01 0.058659716 2.703882e-01 2.991266e-01 3.199344e-01
## 546 VARI 1.708056e-02 0.016435109 7.163965e-03 1.779963e-02 2.903145e-02
## 547 BGI 5.159173e-01 0.063766630 4.829690e-01 5.044009e-01 5.278829e-01
## 548 BI 1.576645e+02 13.721336042 1.495583e+02 1.582037e+02 1.651035e+02
## 549 Blue 9.650251e+01 17.179029550 8.663265e+01 9.479592e+01 1.023673e+02
## 550 GLI 1.588122e-01 0.023509295 1.527699e-01 1.627243e-01 1.710447e-01
## 551 gray 1.724887e+02 13.820421603 1.643430e+02 1.733682e+02 1.805926e+02
## 552 Green 1.861704e+02 13.624547658 1.781429e+02 1.873673e+02 1.948367e+02
## 553 HI 7.408370e-01 0.050689452 7.052632e-01 7.397849e-01 7.737494e-01
## 554 HUE -1.564936e+00 0.036986414 -1.566522e+00 -1.565863e+00 -1.565061e+00
## 555 NGRDI 3.237852e-02 0.007694591 2.758137e-02 3.252652e-02 3.811958e-02
## 556 Red 1.745999e+02 14.179625477 1.664898e+02 1.757143e+02 1.826327e+02
## 557 SCI -3.237852e-02 0.007694591 -3.811958e-02 -3.252652e-02 -2.758137e-02
## 558 SI 2.922959e-01 0.047084031 2.800412e-01 2.996543e-01 3.180760e-01
## 559 VARI 4.392920e-02 0.009710020 3.760518e-02 4.396396e-02 5.151563e-02
## 560 BGI 5.666081e-01 0.066185437 5.313483e-01 5.560075e-01 5.815648e-01
## 561 BI 1.672388e+02 15.254461292 1.578224e+02 1.671204e+02 1.766321e+02
## 562 Blue 1.078770e+02 19.747760866 9.587755e+01 1.055918e+02 1.165510e+02
## 563 GLI 1.197491e-01 0.022304631 1.106805e-01 1.219795e-01 1.330704e-01
## 564 gray 1.803647e+02 15.424924103 1.707613e+02 1.805379e+02 1.904228e+02
## 565 Green 1.893122e+02 15.887831016 1.791429e+02 1.895816e+02 2.003673e+02
## 566 HI 1.030893e+00 0.167196006 9.074960e-01 1.016514e+00 1.163739e+00
## 567 HUE 8.921013e-02 1.530465766 -1.556655e+00 1.472025e+00 1.562077e+00
## 568 NGRDI -3.208784e-03 0.017743870 -1.747636e-02 -1.633745e-03 1.018313e-02
## 569 Red 1.904362e+02 14.821982349 1.813061e+02 1.910204e+02 2.002449e+02
## 570 SCI 3.208784e-03 0.017743870 -1.018313e-02 1.633745e-03 1.747636e-02
## 571 SI 2.815712e-01 0.053005356 2.629915e-01 2.887514e-01 3.138900e-01
## 572 VARI -4.231938e-03 0.024516247 -2.408287e-02 -2.268541e-03 1.417627e-02
## 573 BGI 5.055943e-01 0.073446911 4.700520e-01 4.922193e-01 5.176691e-01
## 574 BI 1.281643e+02 18.853688053 1.168745e+02 1.254604e+02 1.346853e+02
## 575 Blue 7.861401e+01 21.139395268 6.780102e+01 7.522449e+01 8.159184e+01
## 576 GLI 1.740048e-01 0.030266609 1.668375e-01 1.782952e-01 1.895035e-01
## 577 gray 1.407645e+02 18.996462889 1.290265e+02 1.382042e+02 1.481471e+02
## 578 Green 1.536511e+02 18.707555429 1.417959e+02 1.510612e+02 1.619949e+02
## 579 HI 6.128575e-01 0.103030323 5.365854e-01 6.287918e-01 6.766391e-01
## 580 HUE -1.565167e+00 0.003559577 -1.566714e+00 -1.566028e+00 -1.564698e+00
## 581 NGRDI 5.071917e-02 0.016691568 4.042076e-02 4.989788e-02 6.258580e-02
## 582 Red 1.391616e+02 19.824444129 1.269439e+02 1.366020e+02 1.471990e+02
## 583 SCI -5.071917e-02 0.016691568 -6.258580e-02 -4.989788e-02 -4.042076e-02
## 584 SI 2.856032e-01 0.052925437 2.693275e-01 2.942268e-01 3.141070e-01
## 585 VARI 6.865797e-02 0.021810797 5.478477e-02 6.691776e-02 8.472075e-02
## 586 BGI 5.695572e-01 0.063098581 5.349127e-01 5.587229e-01 5.829474e-01
## 587 BI 1.717000e+02 13.650988803 1.640337e+02 1.711276e+02 1.786286e+02
## 588 Blue 1.124495e+02 18.462811866 1.020408e+02 1.097755e+02 1.186327e+02
## 589 GLI 1.277223e-01 0.022444609 1.191524e-01 1.299448e-01 1.420012e-01
## 590 gray 1.857400e+02 13.433976159 1.782143e+02 1.855378e+02 1.930289e+02
## 591 Green 1.965788e+02 13.214038616 1.892653e+02 1.969796e+02 2.040000e+02
## 592 HI 9.041533e-01 0.101551396 8.174045e-01 8.848758e-01 9.536239e-01
## 593 HUE -1.041056e+00 1.127944155 -1.563560e+00 -1.559081e+00 -1.540524e+00
## 594 NGRDI 1.080787e-02 0.011320207 4.620462e-03 1.198167e-02 1.990020e-02
## 595 Red 1.924048e+02 13.362369902 1.847041e+02 1.923673e+02 2.003265e+02
## 596 SCI -1.080787e-02 0.011320207 -1.990020e-02 -1.198167e-02 -4.620462e-03
## 597 SI 2.662022e-01 0.045228656 2.551637e-01 2.741124e-01 2.908000e-01
## 598 VARI 1.505163e-02 0.015658227 6.570312e-03 1.693834e-02 2.800701e-02
## 599 BGI 5.136999e-01 0.067818433 4.833272e-01 5.003712e-01 5.217761e-01
## 600 BI 1.198540e+02 15.102103461 1.123906e+02 1.172470e+02 1.230915e+02
## 601 Blue 7.425426e+01 18.716515505 6.595918e+01 7.106122e+01 7.637755e+01
## 602 GLI 1.685783e-01 0.030022294 1.558108e-01 1.732643e-01 1.882053e-01
## 603 gray 1.314305e+02 14.821883632 1.239274e+02 1.291451e+02 1.352574e+02
## 604 Green 1.430537e+02 14.567105097 1.352653e+02 1.414286e+02 1.478163e+02
## 605 HI 6.382261e-01 0.176387068 4.856875e-01 6.291667e-01 7.384014e-01
## 606 HUE -1.461387e+00 0.531711148 -1.566605e+00 -1.564688e+00 -1.561563e+00
## 607 NGRDI 4.680895e-02 0.024643389 3.050796e-02 4.601382e-02 6.880573e-02
## 608 Red 1.304114e+02 15.213986066 1.219286e+02 1.274082e+02 1.345918e+02
## 609 SCI -4.680895e-02 0.024643389 -6.880573e-02 -4.601382e-02 -3.050796e-02
## 610 SI 2.811871e-01 0.050980546 2.636429e-01 2.847900e-01 3.105250e-01
## 611 VARI 6.386241e-02 0.033348409 4.215504e-02 6.274008e-02 9.372058e-02
## 612 BGI 5.233699e-01 0.071366342 4.852351e-01 5.082110e-01 5.365184e-01
## 613 BI 1.540889e+02 17.517171455 1.424442e+02 1.531945e+02 1.640565e+02
## 614 Blue 9.335171e+01 21.686472899 8.009184e+01 8.948980e+01 1.016122e+02
## 615 GLI 1.357483e-01 0.025168397 1.239059e-01 1.393655e-01 1.524221e-01
## 616 gray 1.671046e+02 18.089294852 1.549565e+02 1.665128e+02 1.779650e+02
## 617 Green 1.767388e+02 19.283769323 1.636531e+02 1.761224e+02 1.888571e+02
## 618 HI 9.959410e-01 0.203502594 8.178332e-01 9.719725e-01 1.165941e+00
## 619 HUE -1.038766e-01 1.543646333 -1.563215e+00 -1.517131e+00 1.561744e+00
## 620 NGRDI 4.205196e-04 0.024052663 -1.981832e-02 3.196535e-03 2.071091e-02
## 621 Red 1.763105e+02 16.493211830 1.652245e+02 1.762245e+02 1.869388e+02
## 622 SCI -4.205196e-04 0.024052663 -2.071091e-02 -3.196535e-03 1.981832e-02
## 623 SI 3.148103e-01 0.062226054 2.904577e-01 3.268891e-01 3.535252e-01
## 624 VARI 1.060480e-03 0.032360432 -2.630472e-02 4.421577e-03 2.886495e-02
## 625 BGI 5.090994e-01 0.074640085 4.719389e-01 4.961193e-01 5.220881e-01
## 626 BI 1.261842e+02 19.187278450 1.141480e+02 1.223852e+02 1.319595e+02
## 627 Blue 7.658158e+01 22.064905577 6.455102e+01 7.152041e+01 7.990306e+01
## 628 GLI 1.579552e-01 0.029446425 1.450048e-01 1.615677e-01 1.764518e-01
## 629 gray 1.378492e+02 19.436966167 1.252792e+02 1.343742e+02 1.445752e+02
## 630 Green 1.483860e+02 19.731447740 1.351633e+02 1.453571e+02 1.560051e+02
## 631 HI 7.845365e-01 0.159833062 6.716482e-01 7.406094e-01 8.911468e-01
## 632 HUE -1.161258e+00 1.021305644 -1.565094e+00 -1.562742e+00 -1.550539e+00
## 633 NGRDI 2.734472e-02 0.021018167 1.268927e-02 3.069510e-02 4.376964e-02
## 634 Red 1.405229e+02 19.127103160 1.281837e+02 1.366531e+02 1.476939e+02
## 635 SCI -2.734472e-02 0.021018167 -4.376964e-02 -3.069510e-02 -1.268927e-02
## 636 SI 3.036723e-01 0.058445554 2.857008e-01 3.136611e-01 3.372737e-01
## 637 VARI 3.699704e-02 0.028058685 1.751993e-02 4.203108e-02 5.874688e-02
## 638 BGI 6.078474e-01 0.053070687 5.768072e-01 5.972071e-01 6.251866e-01
## 639 BI 1.867474e+02 13.368470137 1.806434e+02 1.866594e+02 1.943435e+02
## 640 Blue 1.286178e+02 16.006173385 1.193878e+02 1.264694e+02 1.362449e+02
## 641 GLI 1.133765e-01 0.018648996 1.052220e-01 1.163055e-01 1.247095e-01
## 642 gray 2.009108e+02 13.510402642 1.951436e+02 2.013281e+02 2.087615e+02
## 643 Green 2.112135e+02 13.470802690 2.058980e+02 2.122245e+02 2.191633e+02
## 644 HI 9.306279e-01 0.094401483 8.666211e-01 9.325843e-01 9.744526e-01
## 645 HUE -9.545181e-01 1.190772573 -1.561704e+00 -1.553566e+00 -1.518738e+00
## 646 NGRDI 7.204478e-03 0.009141375 2.319307e-03 7.101086e-03 1.381579e-02
## 647 Red 2.082477e+02 14.195514712 2.020408e+02 2.085918e+02 2.166327e+02
## 648 SCI -7.204478e-03 0.009141375 -1.381579e-02 -7.101086e-03 -2.319307e-03
## 649 SI 2.384427e-01 0.036727602 2.269988e-01 2.445077e-01 2.597786e-01
## 650 VARI 1.027617e-02 0.013126463 3.409407e-03 1.006711e-02 1.984564e-02
## 651 BGI 4.952912e-01 0.083176449 4.551537e-01 4.763584e-01 4.980520e-01
## 652 BI 1.370597e+02 16.845397255 1.276754e+02 1.353550e+02 1.432755e+02
## 653 Blue 8.254839e+01 21.922877265 7.188265e+01 7.875510e+01 8.534694e+01
## 654 GLI 1.783104e-01 0.032992951 1.746986e-01 1.845826e-01 1.950874e-01
## 655 gray 1.507312e+02 16.440247545 1.413634e+02 1.494971e+02 1.576889e+02
## 656 Green 1.648639e+02 15.608272220 1.557755e+02 1.642653e+02 1.721786e+02
## 657 HI 6.140678e-01 0.064753533 5.465704e-01 6.327898e-01 6.645768e-01
## 658 HUE -1.566132e+00 0.010084477 -1.567193e+00 -1.566778e+00 -1.566183e+00
## 659 NGRDI 5.154886e-02 0.013234603 4.475784e-02 5.044159e-02 6.250312e-02
## 660 Red 1.489818e+02 17.125448397 1.387959e+02 1.476327e+02 1.567755e+02
## 661 SCI -5.154886e-02 0.013234603 -6.250312e-02 -5.044159e-02 -4.475784e-02
## 662 SI 2.952387e-01 0.057882639 2.873269e-01 3.077318e-01 3.252201e-01
## 663 VARI 6.910328e-02 0.016349416 5.998907e-02 6.696758e-02 8.424664e-02
## 664 BGI 5.336188e-01 0.069163995 4.974636e-01 5.207194e-01 5.435871e-01
## 665 BI 1.628448e+02 14.040329393 1.551261e+02 1.632000e+02 1.703755e+02
## 666 Blue 1.019810e+02 19.183728951 9.108163e+01 9.983673e+01 1.084082e+02
## 667 GLI 1.473044e-01 0.022631135 1.439975e-01 1.510857e-01 1.584462e-01
## 668 gray 1.774100e+02 14.015288664 1.696882e+02 1.782003e+02 1.856003e+02
## 669 Green 1.900616e+02 14.075069732 1.823061e+02 1.911429e+02 1.987959e+02
## 670 HI 7.994245e-01 0.068526871 7.397882e-01 8.057697e-01 8.494624e-01
## 671 HUE -1.548913e+00 0.198724403 -1.565273e+00 -1.564533e+00 -1.562722e+00
## 672 NGRDI 2.346648e-02 0.007541160 1.899536e-02 2.353198e-02 2.749106e-02
## 673 Red 1.813310e+02 13.270621244 1.743878e+02 1.821837e+02 1.890816e+02
## 674 SCI -2.346648e-02 0.007541160 -2.749106e-02 -2.353198e-02 -1.899536e-02
## 675 SI 2.851300e-01 0.052935122 2.714772e-01 2.935806e-01 3.165445e-01
## 676 VARI 3.228889e-02 0.010389648 2.570829e-02 3.227931e-02 3.721519e-02
## 677 BGI 5.363549e-01 0.066370741 4.990220e-01 5.208711e-01 5.507217e-01
## 678 BI 1.622543e+02 13.517443985 1.535825e+02 1.610629e+02 1.692846e+02
## 679 Blue 1.016159e+02 17.771590643 9.083673e+01 9.851020e+01 1.077755e+02
## 680 GLI 1.441727e-01 0.026540456 1.330787e-01 1.498287e-01 1.615670e-01
## 681 gray 1.765660e+02 13.151570506 1.683397e+02 1.758571e+02 1.838684e+02
## 682 Green 1.886103e+02 12.235751556 1.814082e+02 1.885510e+02 1.957347e+02
## 683 HI 8.399318e-01 0.128388623 7.240705e-01 8.250756e-01 9.142663e-01
## 684 HUE -1.225113e+00 0.941316253 -1.565847e+00 -1.563267e+00 -1.555597e+00
## 685 NGRDI 1.982248e-02 0.015405983 9.541884e-03 2.033617e-02 3.306392e-02
## 686 Red 1.814967e+02 14.877843221 1.715918e+02 1.800000e+02 1.905918e+02
## 687 SCI -1.982248e-02 0.015405983 -3.306392e-02 -2.033617e-02 -9.541884e-03
## 688 SI 2.860697e-01 0.046315678 2.752465e-01 2.956003e-01 3.116864e-01
## 689 VARI 2.697639e-02 0.020994841 1.335392e-02 2.796167e-02 4.544025e-02
## 690 BGI 6.234243e-01 0.051347026 5.922737e-01 6.224598e-01 6.486731e-01
## 691 BI 1.843406e+02 12.407116365 1.776757e+02 1.858557e+02 1.925398e+02
## 692 Blue 1.293257e+02 16.050989182 1.193776e+02 1.301837e+02 1.389592e+02
## 693 GLI 1.058335e-01 0.016277606 9.732254e-02 1.061799e-01 1.163689e-01
## 694 gray 1.976974e+02 12.384748219 1.912227e+02 1.993255e+02 2.059830e+02
## 695 Green 2.068853e+02 12.362877362 2.006224e+02 2.085510e+02 2.151020e+02
## 696 HI 9.713776e-01 0.070392318 9.328708e-01 9.742944e-01 1.000000e+00
## 697 HUE -6.490884e-01 1.295622142 -1.550777e+00 -1.517756e+00 0.000000e+00
## 698 NGRDI 2.770496e-03 0.006281432 0.000000e+00 2.355203e-03 6.779014e-03
## 699 Red 2.057279e+02 12.046072879 1.995306e+02 2.077347e+02 2.138776e+02
## 700 SCI -2.770496e-03 0.006281432 -6.779014e-03 -2.355203e-03 0.000000e+00
## 701 SI 2.305398e-01 0.037646495 2.102963e-01 2.289809e-01 2.537458e-01
## 702 VARI 4.026234e-03 0.009183291 0.000000e+00 3.453866e-03 9.674783e-03
## 703 BGI 5.906602e-01 0.061360229 5.524775e-01 5.819846e-01 6.144118e-01
## 704 BI 1.843845e+02 13.148442961 1.763827e+02 1.839973e+02 1.917711e+02
## 705 Blue 1.211344e+02 17.486057261 1.104796e+02 1.186327e+02 1.285510e+02
## 706 GLI 1.017174e-01 0.018319622 9.203013e-02 1.035036e-01 1.132234e-01
## 707 gray 1.975377e+02 13.297464069 1.894378e+02 1.975564e+02 2.055002e+02
## 708 Green 2.045594e+02 13.395182083 1.964082e+02 2.049592e+02 2.127755e+02
## 709 HI 1.200024e+00 0.124504484 1.132856e+00 1.201386e+00 1.278084e+00
## 710 HUE 1.345772e+00 0.781644949 1.561811e+00 1.564769e+00 1.566465e+00
## 711 NGRDI -1.996159e-02 0.012247129 -2.809257e-02 -2.050877e-02 -1.378561e-02
## 712 Red 2.128829e+02 13.755183913 2.042653e+02 2.130612e+02 2.218980e+02
## 713 SCI 1.996159e-02 0.012247129 1.378561e-02 2.050877e-02 2.809257e-02
## 714 SI 2.775080e-01 0.049154190 2.554118e-01 2.839601e-01 3.094629e-01
## 715 VARI -2.788813e-02 0.017001666 -3.912620e-02 -2.866801e-02 -1.930492e-02
## 716 BGI 4.767877e-01 0.076903317 4.424008e-01 4.592376e-01 4.794548e-01
## 717 BI 1.392395e+02 15.366369679 1.317755e+02 1.375969e+02 1.444194e+02
## 718 Blue 8.129589e+01 20.211299872 7.193878e+01 7.738776e+01 8.410204e+01
## 719 GLI 1.872960e-01 0.030701071 1.849219e-01 1.949402e-01 2.019633e-01
## 720 gray 1.536365e+02 15.083155810 1.461844e+02 1.525704e+02 1.595581e+02
## 721 Green 1.688923e+02 14.408747167 1.617755e+02 1.686531e+02 1.754694e+02
## 722 HI 5.981487e-01 0.046545932 5.505618e-01 6.099713e-01 6.369103e-01
## 723 HUE -1.566952e+00 0.001345488 -1.567621e+00 -1.567265e+00 -1.566838e+00
## 724 NGRDI 5.572581e-02 0.011047152 5.159066e-02 5.675984e-02 6.399175e-02
## 725 Red 1.512674e+02 15.483874450 1.435153e+02 1.495510e+02 1.568367e+02
## 726 SCI -5.572581e-02 0.011047152 -6.399175e-02 -5.675984e-02 -5.159066e-02
## 727 SI 3.083466e-01 0.055223945 3.004248e-01 3.178120e-01 3.355263e-01
## 728 VARI 7.396256e-02 0.013183607 6.808397e-02 7.355888e-02 8.496368e-02
## 729 BGI 6.032492e-01 0.062880036 5.665221e-01 5.984609e-01 6.301392e-01
## 730 BI 1.736074e+02 28.457258013 1.659649e+02 1.813097e+02 1.920285e+02
## 731 Blue 1.163127e+02 26.091302186 1.051224e+02 1.199388e+02 1.320816e+02
## 732 GLI 9.616794e-02 0.045626857 8.176099e-02 1.083292e-01 1.254361e-01
## 733 gray 1.857804e+02 31.707609235 1.792353e+02 1.953846e+02 2.059950e+02
## 734 Green 1.927309e+02 36.476874261 1.885918e+02 2.056531e+02 2.150816e+02
## 735 HI 1.295403e+00 0.725287264 9.073698e-01 1.012385e+00 1.266070e+00
## 736 HUE 6.505451e-02 1.542607590 -1.557563e+00 1.464413e+00 1.565774e+00
## 737 NGRDI -2.190857e-02 0.056443762 -2.370389e-02 -1.214831e-03 9.810347e-03
## 738 Red 1.986211e+02 26.694723101 1.884388e+02 2.043878e+02 2.165204e+02
## 739 SCI 2.190857e-02 0.056443762 -9.810347e-03 1.214831e-03 2.370389e-02
## 740 SI 2.694197e-01 0.059308892 2.421634e-01 2.628703e-01 2.895090e-01
## 741 VARI -3.004305e-02 0.076733608 -3.413615e-02 -1.707213e-03 1.390930e-02
## 742 BGI 6.608370e-01 0.043804392 6.339592e-01 6.554491e-01 6.800178e-01
## 743 BI 1.919331e+02 14.236019575 1.838461e+02 1.934966e+02 2.015874e+02
## 744 Blue 1.387411e+02 15.774465958 1.295306e+02 1.390510e+02 1.479388e+02
## 745 GLI 8.166800e-02 0.017496028 7.120438e-02 8.178244e-02 9.318041e-02
## 746 gray 2.038340e+02 14.767399361 1.957254e+02 2.059767e+02 2.138687e+02
## 747 Green 2.096147e+02 15.624670969 2.016939e+02 2.123469e+02 2.200612e+02
## 748 HI 1.228318e+00 0.243940739 1.068273e+00 1.216686e+00 1.360324e+00
## 749 HUE 1.067478e+00 1.110616393 1.548512e+00 1.563919e+00 1.566450e+00
## 750 NGRDI -1.830239e-02 0.019713099 -2.792836e-02 -1.767597e-02 -5.747744e-03
## 751 Red 2.173034e+02 14.894451577 2.085918e+02 2.181939e+02 2.275102e+02
## 752 SCI 1.830239e-02 0.019713099 5.747744e-03 1.767597e-02 2.792836e-02
## 753 SI 2.223938e-01 0.035662006 2.052356e-01 2.225195e-01 2.423292e-01
## 754 VARI -2.683824e-02 0.028399977 -4.164850e-02 -2.629774e-02 -8.586295e-03
## 755 BGI 4.963050e-01 0.073444184 4.596644e-01 4.764354e-01 5.001154e-01
## 756 BI 1.418642e+02 16.635375975 1.317519e+02 1.386728e+02 1.485321e+02
## 757 Blue 8.440060e+01 20.735227775 7.381633e+01 7.900000e+01 8.697959e+01
## 758 GLI 1.672326e-01 0.029428239 1.573240e-01 1.747358e-01 1.860547e-01
## 759 gray 1.555060e+02 16.393764664 1.453570e+02 1.528568e+02 1.627708e+02
## 760 Green 1.684070e+02 15.653267092 1.587143e+02 1.665918e+02 1.755510e+02
## 761 HI 7.360677e-01 0.120886715 6.470307e-01 6.882540e-01 8.199401e-01
## 762 HUE -1.465058e+00 0.522683401 -1.566622e+00 -1.565708e+00 -1.561889e+00
## 763 NGRDI 3.500862e-02 0.017432143 2.014504e-02 4.046740e-02 4.870738e-02
## 764 Red 1.572891e+02 17.533095896 1.455918e+02 1.540816e+02 1.665714e+02
## 765 SCI -3.500862e-02 0.017432143 -4.870738e-02 -4.046740e-02 -2.014504e-02
## 766 SI 3.083513e-01 0.054457255 2.998362e-01 3.218687e-01 3.385897e-01
## 767 VARI 4.682874e-02 0.022951465 2.751032e-02 5.425122e-02 6.458670e-02
## 768 BGI 5.879011e-01 0.061850301 5.507415e-01 5.762660e-01 6.108035e-01
## 769 BI 1.806679e+02 13.499396994 1.723232e+02 1.811220e+02 1.890370e+02
## 770 Blue 1.188510e+02 18.960246330 1.075408e+02 1.168571e+02 1.272551e+02
## 771 GLI 1.050895e-01 0.018761346 9.529544e-02 1.072040e-01 1.175052e-01
## 772 gray 1.937799e+02 13.618550429 1.852717e+02 1.945903e+02 2.025116e+02
## 773 Green 2.012828e+02 14.193564088 1.925714e+02 2.021837e+02 2.107959e+02
## 774 HI 1.153393e+00 0.178428124 1.021892e+00 1.156526e+00 1.280097e+00
## 775 HUE 8.875292e-01 1.266464458 1.516593e+00 1.563088e+00 1.566288e+00
## 776 NGRDI -1.579716e-02 0.017893962 -2.875175e-02 -1.657241e-02 -2.350120e-03
## 777 Red 2.076182e+02 12.761460651 1.996122e+02 2.081020e+02 2.158673e+02
## 778 SCI 1.579716e-02 0.017893962 2.350120e-03 1.657241e-02 2.875175e-02
## 779 SI 2.757619e-01 0.053393651 2.510746e-01 2.839929e-01 3.098180e-01
## 780 VARI -2.176301e-02 0.024845566 -3.960463e-02 -2.311477e-02 -3.325079e-03
## 781 BGI 5.864472e-01 0.073778484 5.358409e-01 5.760101e-01 6.222618e-01
## 782 BI 1.466735e+02 23.025185723 1.311307e+02 1.484114e+02 1.622069e+02
## 783 Blue 9.536840e+01 24.564633438 7.951020e+01 9.353061e+01 1.083878e+02
## 784 GLI 9.622130e-02 0.060560453 4.670676e-02 1.187224e-01 1.433228e-01
## 785 gray 1.568098e+02 26.091990762 1.402566e+02 1.613986e+02 1.749981e+02
## 786 Green 1.623239e+02 32.050590891 1.453673e+02 1.716122e+02 1.845306e+02
## 787 HI 1.415924e+00 0.925803361 8.165785e-01 9.099307e-01 1.822424e+00
## 788 HUE -3.424832e-01 1.514432586 -1.562261e+00 -1.552045e+00 1.567510e+00
## 789 NGRDI -2.929527e-02 0.075356800 -6.261533e-02 8.569839e-03 2.155741e-02
## 790 Red 1.694101e+02 18.847371714 1.561633e+02 1.688980e+02 1.818776e+02
## 791 SCI 2.929527e-02 0.075356800 -2.155741e-02 -8.569839e-03 6.261533e-02
## 792 SI 2.898002e-01 0.071569873 2.532771e-01 2.851537e-01 3.245449e-01
## 793 VARI -3.936821e-02 0.101748513 -8.886065e-02 1.244002e-02 2.979241e-02
## 794 BGI 6.584637e-01 0.055567861 6.201099e-01 6.504906e-01 6.882962e-01
## 795 BI 1.939397e+02 14.736774250 1.845409e+02 1.949802e+02 2.040201e+02
## 796 Blue 1.413372e+02 18.530469036 1.286837e+02 1.406735e+02 1.527704e+02
## 797 GLI 9.000267e-02 0.018822835 7.859394e-02 9.276735e-02 1.033320e-01
## 798 gray 2.065247e+02 14.496589366 1.975772e+02 2.079326e+02 2.164403e+02
## 799 Green 2.140168e+02 14.112464723 2.056735e+02 2.156633e+02 2.235867e+02
## 800 HI 1.081668e+00 0.115270996 1.003372e+00 1.054054e+00 1.132383e+00
## 801 HUE 8.390084e-01 1.249126982 1.175641e+00 1.544383e+00 1.558319e+00
## 802 NGRDI -6.070114e-03 0.008386717 -1.011802e-02 -4.733728e-03 -2.991629e-04
## 803 Red 2.166702e+02 14.883136897 2.075102e+02 2.180204e+02 2.270765e+02
## 804 SCI 6.070114e-03 0.008386717 2.991629e-04 4.733728e-03 1.011802e-02
## 805 SI 2.130881e-01 0.036944184 1.927199e-01 2.170519e-01 2.374317e-01
## 806 VARI -9.181675e-03 0.012675286 -1.533073e-02 -6.981754e-03 -4.310898e-04
## 807 BGI 5.252285e-01 0.072450206 4.897937e-01 5.116636e-01 5.336676e-01
## 808 BI 1.619602e+02 14.686173970 1.536855e+02 1.618490e+02 1.685587e+02
## 809 Blue 1.000442e+02 20.495266847 8.895918e+01 9.730612e+01 1.049592e+02
## 810 GLI 1.492687e-01 0.025058667 1.444776e-01 1.528915e-01 1.616725e-01
## 811 gray 1.765262e+02 14.432636832 1.682649e+02 1.767711e+02 1.838126e+02
## 812 Green 1.891114e+02 14.083649253 1.807551e+02 1.896735e+02 1.967959e+02
## 813 HI 8.177332e-01 0.080804091 7.378599e-01 8.166704e-01 8.777977e-01
## 814 HUE -1.534086e+00 0.273919485 -1.565714e+00 -1.563684e+00 -1.560046e+00
## 815 NGRDI 2.208385e-02 0.010173725 1.420980e-02 2.185759e-02 3.021689e-02
## 816 Red 1.809793e+02 14.019428440 1.733673e+02 1.815102e+02 1.885102e+02
## 817 SCI -2.208385e-02 0.010173725 -3.021689e-02 -2.185759e-02 -1.420980e-02
## 818 SI 2.940206e-01 0.055638997 2.807332e-01 3.035689e-01 3.251066e-01
## 819 VARI 3.010799e-02 0.013759882 1.942462e-02 2.997483e-02 4.163301e-02
## 820 BGI 6.644490e-01 0.058889432 6.194996e-01 6.552901e-01 7.117511e-01
## 821 BI 1.924538e+02 17.435830759 1.802988e+02 1.918905e+02 2.068018e+02
## 822 Blue 1.413701e+02 21.864834438 1.250408e+02 1.384898e+02 1.598163e+02
## 823 GLI 8.727709e-02 0.018279757 7.260718e-02 8.959835e-02 1.015088e-01
## 824 gray 2.046374e+02 17.179843445 1.928910e+02 2.046483e+02 2.186620e+02
## 825 Green 2.116941e+02 16.922437595 2.002857e+02 2.122857e+02 2.251224e+02
## 826 HI 1.096857e+00 0.103782669 1.025075e+00 1.094980e+00 1.174312e+00
## 827 HUE 1.009635e+00 1.155492078 1.518767e+00 1.554558e+00 1.561193e+00
## 828 NGRDI -7.626938e-03 0.008270597 -1.268319e-02 -8.263614e-03 -2.434299e-03
## 829 Red 2.149054e+02 16.695046967 2.037347e+02 2.147347e+02 2.284286e+02
## 830 SCI 7.626938e-03 0.008270597 2.434299e-03 8.263614e-03 1.268319e-02
## 831 SI 2.103683e-01 0.042225532 1.761900e-01 2.139093e-01 2.414926e-01
## 832 VARI -1.138427e-02 0.012260612 -1.959698e-02 -1.211563e-02 -3.526702e-03
## 833 BGI 5.027097e-01 0.067334059 4.714096e-01 4.927040e-01 5.119752e-01
## 834 BI 1.505484e+02 13.970945293 1.425070e+02 1.513280e+02 1.585097e+02
## 835 Blue 8.919961e+01 18.557558081 7.937245e+01 8.804082e+01 9.519898e+01
## 836 GLI 1.540684e-01 0.025207854 1.450773e-01 1.583017e-01 1.685665e-01
## 837 gray 1.643874e+02 14.214106207 1.560261e+02 1.657379e+02 1.732905e+02
## 838 Green 1.762330e+02 14.988840699 1.673316e+02 1.781020e+02 1.866327e+02
## 839 HI 8.591382e-01 0.146497602 7.342044e-01 8.253012e-01 9.521445e-01
## 840 HUE -9.404625e-01 1.226693297 -1.565763e+00 -1.563230e+00 -1.539686e+00
## 841 NGRDI 1.815431e-02 0.018876351 5.397814e-03 2.246767e-02 3.335130e-02
## 842 Red 1.697989e+02 12.833991451 1.623265e+02 1.709898e+02 1.773827e+02
## 843 SCI -1.815431e-02 0.018876351 -3.335130e-02 -2.246767e-02 -5.397814e-03
## 844 SI 3.170402e-01 0.054824593 3.002515e-01 3.211826e-01 3.471662e-01
## 845 VARI 2.447713e-02 0.025187399 7.542159e-03 3.000085e-02 4.493357e-02
## 846 BGI 5.401996e-01 0.060484950 5.093409e-01 5.291851e-01 5.499103e-01
## 847 BI 1.717271e+02 13.543024645 1.639424e+02 1.711667e+02 1.783935e+02
## 848 Blue 1.086757e+02 18.247335771 9.857143e+01 1.062041e+02 1.140000e+02
## 849 GLI 1.453791e-01 0.021072499 1.404852e-01 1.495929e-01 1.568516e-01
## 850 gray 1.869982e+02 13.364030048 1.792988e+02 1.869496e+02 1.941124e+02
## 851 Green 2.002204e+02 13.033207500 1.927959e+02 2.007755e+02 2.075918e+02
## 852 HI 7.962183e-01 0.065145621 7.199483e-01 8.064516e-01 8.494624e-01
## 853 HUE -1.562826e+00 0.051495215 -1.566236e+00 -1.564793e+00 -1.563266e+00
## 854 NGRDI 2.395892e-02 0.008153070 1.838009e-02 2.299750e-02 3.020949e-02
## 855 Red 1.909022e+02 13.258302149 1.833878e+02 1.904898e+02 1.981633e+02
## 856 SCI -2.395892e-02 0.008153070 -3.020949e-02 -2.299750e-02 -1.838009e-02
## 857 SI 2.785010e-01 0.045531620 2.668452e-01 2.842253e-01 3.037266e-01
## 858 VARI 3.303924e-02 0.011078534 2.513405e-02 3.173461e-02 4.225260e-02
## 859 BGI 5.095070e-01 0.080433409 4.679955e-01 4.899000e-01 5.165715e-01
## 860 BI 1.407327e+02 17.775659232 1.297490e+02 1.366746e+02 1.480760e+02
## 861 Blue 8.506327e+01 22.686944448 7.267857e+01 7.934694e+01 8.964286e+01
## 862 GLI 1.555301e-01 0.028867897 1.491384e-01 1.611795e-01 1.713214e-01
## 863 gray 1.536071e+02 17.583689370 1.424809e+02 1.496647e+02 1.618749e+02
## 864 Green 1.649445e+02 17.174186065 1.539592e+02 1.609592e+02 1.739847e+02
## 865 HI 8.145347e-01 0.119336862 7.205048e-01 8.114187e-01 8.917800e-01
## 866 HUE -1.350941e+00 0.761779914 -1.564849e+00 -1.562290e+00 -1.555784e+00
## 867 NGRDI 2.355922e-02 0.015079984 1.317728e-02 2.407351e-02 3.497375e-02
## 868 Red 1.574831e+02 17.663902522 1.458827e+02 1.546531e+02 1.663418e+02
## 869 SCI -2.355922e-02 0.015079984 -3.497375e-02 -2.407351e-02 -1.317728e-02
## 870 SI 3.072162e-01 0.061912014 2.924830e-01 3.218014e-01 3.437263e-01
## 871 VARI 3.176585e-02 0.020371423 1.780102e-02 3.240083e-02 4.737910e-02
## 872 BGI 6.381156e-01 0.056982616 6.025020e-01 6.348491e-01 6.659326e-01
## 873 BI 1.812765e+02 15.467716855 1.719753e+02 1.828488e+02 1.913189e+02
## 874 Blue 1.293654e+02 19.008309869 1.173673e+02 1.295306e+02 1.402041e+02
## 875 GLI 9.896265e-02 0.020212190 8.684980e-02 9.868596e-02 1.104740e-01
## 876 gray 1.937770e+02 15.291710174 1.846557e+02 1.957343e+02 2.038467e+02
## 877 Green 2.018648e+02 14.938236710 1.931020e+02 2.040000e+02 2.116531e+02
## 878 HI 1.022236e+00 0.118894477 9.538188e-01 1.042996e+00 1.108108e+00
## 879 HUE 3.096260e-01 1.494619069 -1.538058e+00 1.534416e+00 1.555965e+00
## 880 NGRDI -1.339624e-03 0.010780131 -9.105692e-03 -3.662333e-03 4.397402e-03
## 881 Red 2.024572e+02 15.590105378 1.932041e+02 2.047143e+02 2.129388e+02
## 882 SCI 1.339624e-03 0.010780131 -4.397402e-03 3.662333e-03 9.105692e-03
## 883 SI 2.236804e-01 0.039405350 2.036586e-01 2.251856e-01 2.474863e-01
## 884 VARI -2.104214e-03 0.015698941 -1.357954e-02 -5.446467e-03 6.428597e-03
## 885 BGI 5.210521e-01 0.080448550 4.780648e-01 4.984161e-01 5.299018e-01
## 886 BI 1.271799e+02 17.810726833 1.167866e+02 1.235113e+02 1.327078e+02
## 887 Blue 7.827965e+01 21.283298058 6.738776e+01 7.326531e+01 8.123469e+01
## 888 GLI 1.522480e-01 0.041884703 1.490965e-01 1.648938e-01 1.749898e-01
## 889 gray 1.385866e+02 17.576059017 1.280797e+02 1.353327e+02 1.442482e+02
## 890 Green 1.486326e+02 17.443377170 1.383776e+02 1.460204e+02 1.548571e+02
## 891 HI 8.416389e-01 0.455435999 6.749029e-01 7.158597e-01 8.195212e-01
## 892 HUE -1.261554e+00 0.914286777 -1.564590e+00 -1.563423e+00 -1.559197e+00
## 893 NGRDI 2.440967e-02 0.038159032 2.167239e-02 3.658354e-02 4.264516e-02
## 894 Red 1.418575e+02 19.384455288 1.293878e+02 1.372449e+02 1.493367e+02
## 895 SCI -2.440967e-02 0.038159032 -4.264516e-02 -3.658354e-02 -2.167239e-02
## 896 SI 2.963610e-01 0.056637459 2.840955e-01 3.080511e-01 3.255671e-01
## 897 VARI 3.288493e-02 0.052562000 2.941903e-02 4.950771e-02 5.754157e-02
## 898 BGI 5.453743e-01 0.060725423 5.159841e-01 5.383914e-01 5.587139e-01
## 899 BI 1.679055e+02 13.738553642 1.602777e+02 1.689485e+02 1.757478e+02
## 900 Blue 1.062119e+02 17.615024466 9.651020e+01 1.055918e+02 1.134286e+02
## 901 GLI 1.371677e-01 0.021175504 1.299605e-01 1.394883e-01 1.484421e-01
## 902 gray 1.823077e+02 13.864531741 1.746202e+02 1.836658e+02 1.907016e+02
## 903 Green 1.938567e+02 13.973839530 1.860204e+02 1.955306e+02 2.027347e+02
## 904 HI 8.823504e-01 0.106801179 8.050383e-01 8.797092e-01 9.551044e-01
## 905 HUE -1.131290e+00 1.037107801 -1.564435e+00 -1.560164e+00 -1.542158e+00
## 906 NGRDI 1.362842e-02 0.012242606 4.942275e-03 1.379203e-02 2.276527e-02
## 907 Red 1.886478e+02 13.716253402 1.812449e+02 1.902041e+02 1.968980e+02
## 908 SCI -1.362842e-02 0.012242606 -2.276527e-02 -1.379203e-02 -4.942275e-03
## 909 SI 2.835643e-01 0.047315931 2.690711e-01 2.876748e-01 3.082603e-01
## 910 VARI 1.885334e-02 0.016919476 6.899720e-03 1.909397e-02 3.162754e-02
## 911 BGI 6.098708e-01 0.048515839 5.858978e-01 6.034535e-01 6.211456e-01
## 912 BI 1.823296e+02 13.962473545 1.751734e+02 1.834618e+02 1.906842e+02
## 913 Blue 1.242767e+02 16.088114131 1.157551e+02 1.240204e+02 1.318163e+02
## 914 GLI 1.024320e-01 0.017583817 9.683495e-02 1.056287e-01 1.127174e-01
## 915 gray 1.953989e+02 14.446472862 1.882687e+02 1.969838e+02 2.042943e+02
## 916 Green 2.032988e+02 15.191455491 1.962551e+02 2.053265e+02 2.127347e+02
## 917 HI 1.101349e+00 0.190407192 9.750000e-01 1.074074e+00 1.185532e+00
## 918 HUE 6.642535e-01 1.388298867 -1.515729e+00 1.552404e+00 1.563644e+00
## 919 NGRDI -9.330426e-03 0.016570835 -1.789507e-02 -7.035849e-03 2.367722e-03
## 920 Red 2.070067e+02 14.162906822 1.997143e+02 2.080204e+02 2.154898e+02
## 921 SCI 9.330426e-03 0.016570835 -2.367722e-03 7.035849e-03 1.789507e-02
## 922 SI 2.520741e-01 0.038316149 2.395319e-01 2.539443e-01 2.715795e-01
## 923 VARI -1.319924e-02 0.023501379 -2.553830e-02 -1.015334e-02 3.458256e-03
## 924 BGI 5.775280e-01 0.059268366 5.466155e-01 5.684733e-01 5.917077e-01
## 925 BI 1.798084e+02 13.804685710 1.713951e+02 1.805878e+02 1.888358e+02
## 926 Blue 1.160994e+02 17.545167739 1.059337e+02 1.151633e+02 1.240612e+02
## 927 GLI 1.065681e-01 0.019658257 9.628659e-02 1.085298e-01 1.187541e-01
## 928 gray 1.929994e+02 13.980393668 1.846489e+02 1.939929e+02 2.024705e+02
## 929 Green 2.003368e+02 14.118793944 1.922194e+02 2.014286e+02 2.099694e+02
## 930 HI 1.181091e+00 0.170105717 1.070511e+00 1.180301e+00 1.300244e+00
## 931 HUE 1.074290e+00 1.115935823 1.550280e+00 1.563870e+00 1.566862e+00
## 932 NGRDI -1.855927e-02 0.016684623 -3.099037e-02 -1.903932e-02 -6.918338e-03
## 933 Red 2.079141e+02 14.682147471 1.988520e+02 2.091633e+02 2.182857e+02
## 934 SCI 1.855927e-02 0.016684623 6.918338e-03 1.903932e-02 3.099037e-02
## 935 SI 2.864660e-01 0.048097403 2.748939e-01 2.934670e-01 3.118238e-01
## 936 VARI -2.565464e-02 0.023132179 -4.302774e-02 -2.624358e-02 -9.655805e-03
## 937 BGI 4.988361e-01 0.074401554 4.642952e-01 4.861383e-01 5.067953e-01
## 938 BI 1.355695e+02 17.247869426 1.263497e+02 1.341900e+02 1.412329e+02
## 939 Blue 8.162961e+01 21.147861264 7.140816e+01 7.851020e+01 8.481633e+01
## 940 GLI 1.712671e-01 0.030139937 1.637478e-01 1.754849e-01 1.874303e-01
## 941 gray 1.487775e+02 16.999392020 1.394489e+02 1.476704e+02 1.551090e+02
## 942 Green 1.617816e+02 16.236082165 1.527551e+02 1.609184e+02 1.682041e+02
## 943 HI 6.774191e-01 0.121874296 5.974020e-01 6.708747e-01 7.426549e-01
## 944 HUE -1.520831e+00 0.359287687 -1.566691e+00 -1.565826e+00 -1.564166e+00
## 945 NGRDI 4.270895e-02 0.018104473 3.172117e-02 4.405891e-02 5.501470e-02
## 946 Red 1.488492e+02 18.058860357 1.383520e+02 1.476327e+02 1.566327e+02
## 947 SCI -4.270895e-02 0.018104473 -5.501470e-02 -4.405891e-02 -3.172117e-02
## 948 SI 2.991275e-01 0.055117447 2.839054e-01 3.075274e-01 3.302507e-01
## 949 VARI 5.741578e-02 0.023915457 4.302970e-02 5.902586e-02 7.356978e-02
## 950 BGI 6.452998e-01 0.045936947 6.177228e-01 6.405857e-01 6.623352e-01
## 951 BI 1.866355e+02 12.561795684 1.787373e+02 1.879469e+02 1.951089e+02
## 952 Blue 1.335245e+02 14.971982518 1.240816e+02 1.336327e+02 1.418571e+02
## 953 GLI 9.298681e-02 0.014564540 8.694741e-02 9.459459e-02 1.019445e-01
## 954 gray 1.991362e+02 12.779960596 1.912188e+02 2.006840e+02 2.079671e+02
## 955 Green 2.065551e+02 13.098926523 1.984490e+02 2.081633e+02 2.155510e+02
## 956 HI 1.087286e+00 0.096963535 1.027787e+00 1.075134e+00 1.124432e+00
## 957 HUE 1.057341e+00 1.096607559 1.520282e+00 1.551048e+00 1.557895e+00
## 958 NGRDI -7.409015e-03 0.007937124 -1.060440e-02 -6.675982e-03 -2.554079e-03
## 959 Red 2.095872e+02 12.471785917 2.018367e+02 2.112653e+02 2.181735e+02
## 960 SCI 7.409015e-03 0.007937124 2.554079e-03 6.675982e-03 1.060440e-02
## 961 SI 2.235270e-01 0.032708226 2.100503e-01 2.258321e-01 2.434022e-01
## 962 VARI -1.088981e-02 0.011693814 -1.542738e-02 -9.900166e-03 -3.674180e-03
## 963 BGI 4.926107e-01 0.076055516 4.584638e-01 4.762635e-01 4.967913e-01
## 964 BI 1.449054e+02 15.102726740 1.368226e+02 1.435785e+02 1.503030e+02
## 965 Blue 8.557046e+01 20.320668320 7.593878e+01 8.185714e+01 8.810204e+01
## 966 GLI 1.684179e-01 0.029608806 1.596530e-01 1.722198e-01 1.855755e-01
## 967 gray 1.589155e+02 14.771443191 1.510003e+02 1.579749e+02 1.649268e+02
## 968 Green 1.722092e+02 14.180157965 1.646735e+02 1.718163e+02 1.787347e+02
## 969 HI 7.372966e-01 0.110425505 6.428760e-01 7.145973e-01 8.276914e-01
## 970 HUE -1.543911e+00 0.236156202 -1.566825e+00 -1.565429e+00 -1.562419e+00
## 971 NGRDI 3.483607e-02 0.016167620 2.112037e-02 3.589904e-02 4.866875e-02
## 972 Red 1.607814e+02 15.298838110 1.517092e+02 1.598367e+02 1.680663e+02
## 973 SCI -3.483607e-02 0.016167620 -4.866875e-02 -3.589904e-02 -2.112037e-02
## 974 SI 3.121250e-01 0.057069655 3.029319e-01 3.241564e-01 3.414389e-01
## 975 VARI 4.650008e-02 0.021178488 2.848770e-02 4.808487e-02 6.453123e-02
## 976 BGI 5.500808e-01 0.059219728 5.190774e-01 5.385491e-01 5.614153e-01
## 977 BI 1.698625e+02 13.085738901 1.620775e+02 1.697667e+02 1.766736e+02
## 978 Blue 1.077964e+02 17.529332955 9.781633e+01 1.056429e+02 1.135510e+02
## 979 GLI 1.325800e-01 0.020327091 1.250778e-01 1.367276e-01 1.448008e-01
## 980 gray 1.841312e+02 12.984661127 1.763847e+02 1.844599e+02 1.912380e+02
## 981 Green 1.950923e+02 12.850621842 1.874898e+02 1.959184e+02 2.024031e+02
## 982 HI 9.238338e-01 0.096337608 8.541667e-01 9.227202e-01 9.837735e-01
## 983 HUE -9.181693e-01 1.221064614 -1.562356e+00 -1.554486e+00 -1.492483e+00
## 984 NGRDI 8.760708e-03 0.010721637 1.787367e-03 8.883714e-03 1.697813e-02
## 985 Red 1.917166e+02 12.839919611 1.838418e+02 1.917551e+02 1.992398e+02
## 986 SCI -8.760708e-03 0.010721637 -1.697813e-02 -8.883714e-03 -1.787367e-03
## 987 SI 2.839748e-01 0.045120413 2.738891e-01 2.908836e-01 3.082561e-01
## 988 VARI 1.211667e-02 0.014837371 2.454060e-03 1.219110e-02 2.348303e-02
## 989 BGI 5.530584e-01 0.070546213 5.162736e-01 5.370370e-01 5.616927e-01
## 990 BI 1.636566e+02 15.838554294 1.579698e+02 1.650233e+02 1.709376e+02
## 991 Blue 1.023545e+02 19.386269513 9.268367e+01 1.005510e+02 1.077755e+02
## 992 GLI 1.184679e-01 0.033712880 1.154251e-01 1.264874e-01 1.365962e-01
## 993 gray 1.764643e+02 16.981708570 1.711931e+02 1.787695e+02 1.847959e+02
## 994 Green 1.847021e+02 19.405651011 1.799184e+02 1.880612e+02 1.943673e+02
## 995 HI 1.138675e+00 0.415889261 9.411413e-01 1.060355e+00 1.154928e+00
## 996 HUE 4.776940e-01 1.457560413 -1.546702e+00 1.549146e+00 1.562554e+00
## 997 NGRDI -1.195716e-02 0.033378550 -1.785921e-02 -6.830836e-03 6.244229e-03
## 998 Red 1.885476e+02 14.463469695 1.824490e+02 1.896122e+02 1.960204e+02
## 999 SCI 1.195716e-02 0.033378550 -6.244229e-03 6.830836e-03 1.785921e-02
## 1000 SI 3.011107e-01 0.054735114 2.879121e-01 3.094519e-01 3.306392e-01
## 1001 VARI -1.632520e-02 0.046416696 -2.416447e-02 -9.290173e-03 8.861186e-03
## 1002 BGI 5.520141e-01 0.062813968 5.161113e-01 5.406273e-01 5.711589e-01
## 1003 BI 1.704044e+02 14.084085540 1.610973e+02 1.696448e+02 1.790454e+02
## 1004 Blue 1.085908e+02 18.788523149 9.677551e+01 1.058980e+02 1.172857e+02
## 1005 GLI 1.323218e-01 0.020451757 1.255564e-01 1.350708e-01 1.441073e-01
## 1006 gray 1.846741e+02 14.015993053 1.753553e+02 1.842030e+02 1.936099e+02
## 1007 Green 1.956796e+02 13.874220670 1.864490e+02 1.955918e+02 2.048367e+02
## 1008 HI 9.170678e-01 0.084334210 8.595944e-01 9.310345e-01 9.787234e-01
## 1009 HUE -1.011148e+00 1.123121443 -1.562048e+00 -1.553444e+00 -1.516643e+00
## 1010 NGRDI 9.298746e-03 0.009423585 2.538820e-03 8.137282e-03 1.608670e-02
## 1011 Red 1.920764e+02 13.667670877 1.832449e+02 1.917959e+02 2.006939e+02
## 1012 SCI -9.298746e-03 0.009423585 -1.608670e-02 -8.137282e-03 -2.538820e-03
## 1013 SI 2.820562e-01 0.048708887 2.634109e-01 2.886659e-01 3.114409e-01
## 1014 VARI 1.293003e-02 0.013072203 3.470709e-03 1.107428e-02 2.210629e-02
## 1015 BGI 5.256734e-01 0.072080453 4.904160e-01 5.131542e-01 5.387520e-01
## 1016 BI 1.209879e+02 16.247767355 1.118147e+02 1.189464e+02 1.263970e+02
## 1017 Blue 7.622281e+01 19.336986929 6.736735e+01 7.324490e+01 7.971429e+01
## 1018 GLI 1.626774e-01 0.029979247 1.517999e-01 1.672948e-01 1.794422e-01
## 1019 gray 1.323620e+02 15.897821032 1.232632e+02 1.306054e+02 1.381737e+02
## 1020 Green 1.435045e+02 14.965749856 1.349796e+02 1.419184e+02 1.491735e+02
## 1021 HI 6.505509e-01 0.164303956 5.142857e-01 6.686013e-01 7.565742e-01
## 1022 HUE -1.506102e+00 0.403182575 -1.565822e+00 -1.564269e+00 -1.561532e+00
## 1023 NGRDI 4.373226e-02 0.022183631 2.795653e-02 4.204893e-02 6.274719e-02
## 1024 Red 1.318912e+02 17.594686655 1.206122e+02 1.299388e+02 1.397959e+02
## 1025 SCI -4.373226e-02 0.022183631 -6.274719e-02 -4.204893e-02 -2.795653e-02
## 1026 SI 2.736746e-01 0.053357396 2.541083e-01 2.808922e-01 3.038643e-01
## 1027 VARI 6.007716e-02 0.030307970 3.888750e-02 5.708035e-02 8.643853e-02
## 1028 BGI 5.578750e-01 0.060432132 5.296891e-01 5.460820e-01 5.657868e-01
## 1029 BI 1.735987e+02 12.539184577 1.677573e+02 1.740112e+02 1.801916e+02
## 1030 Blue 1.118281e+02 16.923042593 1.034694e+02 1.099592e+02 1.169388e+02
## 1031 GLI 1.329989e-01 0.020295599 1.279804e-01 1.369193e-01 1.437639e-01
## 1032 gray 1.882076e+02 12.446354665 1.826066e+02 1.890148e+02 1.951364e+02
## 1033 Green 1.997972e+02 12.252480796 1.942959e+02 2.009184e+02 2.067959e+02
## 1034 HI 8.807755e-01 0.078175032 8.305580e-01 8.645161e-01 9.274880e-01
## 1035 HUE -1.299025e+00 0.837040105 -1.563329e+00 -1.561718e+00 -1.553874e+00
## 1036 NGRDI 1.336452e-02 0.008493947 8.130490e-03 1.532367e-02 1.875696e-02
## 1037 Red 1.945761e+02 12.667674389 1.888878e+02 1.954286e+02 2.020408e+02
## 1038 SCI -1.336452e-02 0.008493947 -1.875696e-02 -1.532367e-02 -8.130490e-03
## 1039 SI 2.732726e-01 0.044168561 2.656649e-01 2.809121e-01 2.957317e-01
## 1040 VARI 1.858073e-02 0.011769841 1.119082e-02 2.138182e-02 2.583999e-02
## 1041 BGI 5.684252e-01 0.063482799 5.336771e-01 5.566303e-01 5.816006e-01
## 1042 BI 1.675780e+02 14.294929108 1.585796e+02 1.671015e+02 1.749552e+02
## 1043 Blue 1.091996e+02 18.916455546 9.734694e+01 1.065306e+02 1.158367e+02
## 1044 GLI 1.250669e-01 0.021061428 1.184196e-01 1.284107e-01 1.373935e-01
## 1045 gray 1.811104e+02 14.297603350 1.721679e+02 1.810355e+02 1.891966e+02
## 1046 Green 1.911642e+02 14.519567832 1.821020e+02 1.917551e+02 1.998980e+02
## 1047 HI 9.447771e-01 0.109967707 8.652215e-01 9.310345e-01 1.008479e+00
## 1048 HUE -6.989166e-01 1.356095320 -1.560914e+00 -1.552022e+00 1.406263e+00
## 1049 NGRDI 6.067767e-03 0.011733720 -9.096260e-04 7.880770e-03 1.491436e-02
## 1050 Red 1.887903e+02 13.471665480 1.803265e+02 1.885510e+02 1.964898e+02
## 1051 SCI -6.067767e-03 0.011733720 -1.491436e-02 -7.880770e-03 9.096260e-04
## 1052 SI 2.714593e-01 0.047930791 2.563863e-01 2.790943e-01 3.006797e-01
## 1053 VARI 8.537689e-03 0.016324603 -1.285990e-03 1.091068e-02 2.093258e-02
## 1054 BGI 5.538897e-01 0.064125987 5.183250e-01 5.382866e-01 5.640308e-01
## 1055 BI 1.658646e+02 14.583505573 1.572537e+02 1.636151e+02 1.724584e+02
## 1056 Blue 1.066650e+02 19.248572480 9.528571e+01 1.021429e+02 1.123673e+02
## 1057 GLI 1.363094e-01 0.022715745 1.297168e-01 1.413093e-01 1.501980e-01
## 1058 gray 1.799891e+02 14.437187462 1.715123e+02 1.781253e+02 1.867808e+02
## 1059 Green 1.915183e+02 14.304672360 1.831429e+02 1.899592e+02 1.985102e+02
## 1060 HI 8.562103e-01 0.101101371 7.977528e-01 8.248540e-01 9.136449e-01
## 1061 HUE -1.227100e+00 0.939637223 -1.564605e+00 -1.562888e+00 -1.555246e+00
## 1062 NGRDI 1.652142e-02 0.011335245 9.770929e-03 1.884648e-02 2.436599e-02
## 1063 Red 1.853111e+02 14.115508665 1.769184e+02 1.838980e+02 1.926531e+02
## 1064 SCI -1.652142e-02 0.011335245 -2.436599e-02 -1.884648e-02 -9.770929e-03
## 1065 SI 2.739631e-01 0.047236529 2.630824e-01 2.855556e-01 3.012643e-01
## 1066 VARI 2.290355e-02 0.015704551 1.366102e-02 2.663980e-02 3.336905e-02
## 1067 BGI 5.307067e-01 0.079491129 4.908226e-01 5.115637e-01 5.391858e-01
## 1068 BI 1.244050e+02 17.524930639 1.145777e+02 1.205029e+02 1.287368e+02
## 1069 Blue 7.965665e+01 22.016196402 6.855102e+01 7.406122e+01 8.216327e+01
## 1070 GLI 1.657628e-01 0.033222430 1.550760e-01 1.715281e-01 1.864473e-01
## 1071 gray 1.361650e+02 16.929042596 1.263868e+02 1.324612e+02 1.411490e+02
## 1072 Green 1.481796e+02 15.940682445 1.390204e+02 1.443776e+02 1.533367e+02
## 1073 HI 5.915352e-01 0.141259564 4.863329e-01 5.584580e-01 6.771488e-01
## 1074 HUE -1.545667e+00 0.229160113 -1.566612e+00 -1.565777e+00 -1.563666e+00
## 1075 NGRDI 5.114085e-02 0.021219066 3.613851e-02 5.408739e-02 6.786963e-02
## 1076 Red 1.341226e+02 17.920954201 1.229592e+02 1.310612e+02 1.401684e+02
## 1077 SCI -5.114085e-02 0.021219066 -6.786963e-02 -5.408739e-02 -3.613851e-02
## 1078 SI 2.630327e-01 0.054356949 2.480852e-01 2.730803e-01 2.915535e-01
## 1079 VARI 7.041398e-02 0.028409694 5.034989e-02 7.449017e-02 9.261343e-02
## 1080 BGI 5.090541e-01 0.071621979 4.742478e-01 4.918278e-01 5.171235e-01
## 1081 BI 1.546645e+02 13.854847481 1.473701e+02 1.535954e+02 1.598453e+02
## 1082 Blue 9.243691e+01 19.838070394 8.279592e+01 8.853061e+01 9.544388e+01
## 1083 GLI 1.502503e-01 0.025659272 1.416734e-01 1.544467e-01 1.651539e-01
## 1084 gray 1.686061e+02 13.449444388 1.615386e+02 1.679831e+02 1.743067e+02
## 1085 Green 1.802210e+02 12.955885865 1.731633e+02 1.799898e+02 1.859388e+02
## 1086 HI 8.772614e-01 0.125202746 7.808689e-01 8.820645e-01 9.664151e-01
## 1087 HUE -1.048378e+00 1.110732610 -1.564535e+00 -1.559310e+00 -1.530627e+00
## 1088 NGRDI 1.534138e-02 0.015740026 3.931639e-03 1.422708e-02 2.710640e-02
## 1089 Red 1.748448e+02 13.527314646 1.669388e+02 1.746735e+02 1.817500e+02
## 1090 SCI -1.534138e-02 0.015740026 -2.710640e-02 -1.422708e-02 -3.931639e-03
## 1091 SI 3.142759e-01 0.056796849 3.062803e-01 3.269907e-01 3.439769e-01
## 1092 VARI 2.072381e-02 0.021160936 5.361728e-03 1.921964e-02 3.683487e-02
## 1093 BGI 5.005302e-01 0.071717117 4.678733e-01 4.825143e-01 5.007282e-01
## 1094 BI 1.518064e+02 13.519412082 1.438607e+02 1.502683e+02 1.571903e+02
## 1095 Blue 9.037774e+01 19.070757928 8.095918e+01 8.640816e+01 9.316327e+01
## 1096 GLI 1.623223e-01 0.026338556 1.585465e-01 1.683177e-01 1.766429e-01
## 1097 gray 1.662064e+02 13.121014884 1.583341e+02 1.650341e+02 1.720948e+02
## 1098 Green 1.794100e+02 12.543613115 1.719592e+02 1.787143e+02 1.855051e+02
## 1099 HI 7.710813e-01 0.083588834 7.059590e-01 7.467045e-01 8.178881e-01
## 1100 HUE -1.532019e+00 0.291010205 -1.566422e+00 -1.565284e+00 -1.562901e+00
## 1101 NGRDI 2.961158e-02 0.011750087 2.125827e-02 3.067285e-02 3.940839e-02
## 1102 Red 1.691962e+02 13.394407166 1.605153e+02 1.681939e+02 1.759796e+02
## 1103 SCI -2.961158e-02 0.011750087 -3.940839e-02 -3.067285e-02 -2.125827e-02
## 1104 SI 3.091705e-01 0.053402042 3.045200e-01 3.221730e-01 3.352795e-01
## 1105 VARI 3.969639e-02 0.015437608 2.922158e-02 4.142549e-02 5.250423e-02
## 1106 BGI 4.943633e-01 0.077755608 4.569874e-01 4.762338e-01 5.018304e-01
## 1107 BI 1.500091e+02 16.346053182 1.388821e+02 1.478448e+02 1.580539e+02
## 1108 Blue 8.848824e+01 21.326640718 7.678571e+01 8.391837e+01 9.352041e+01
## 1109 GLI 1.644287e-01 0.029371844 1.578504e-01 1.702500e-01 1.807206e-01
## 1110 gray 1.642785e+02 16.145885123 1.530176e+02 1.624805e+02 1.731732e+02
## 1111 Green 1.774038e+02 15.624160188 1.666735e+02 1.760408e+02 1.867245e+02
## 1112 HI 7.767000e-01 0.116010100 6.838015e-01 7.543328e-01 8.530537e-01
## 1113 HUE -1.432486e+00 0.609356500 -1.566493e+00 -1.565250e+00 -1.561781e+00
## 1114 NGRDI 2.952168e-02 0.015972060 1.908002e-02 3.129810e-02 4.170809e-02
## 1115 Red 1.674074e+02 16.832797093 1.552857e+02 1.657755e+02 1.772041e+02
## 1116 SCI -2.952168e-02 0.015972060 -4.170809e-02 -3.129810e-02 -1.908002e-02
## 1117 SI 3.153788e-01 0.058775028 3.015591e-01 3.254717e-01 3.476195e-01
## 1118 VARI 3.938436e-02 0.021129887 2.549999e-02 4.173968e-02 5.583562e-02
## 1119 BGI 4.813056e-01 0.071723892 4.491575e-01 4.649947e-01 4.836746e-01
## 1120 BI 1.381362e+02 15.090175072 1.303772e+02 1.362351e+02 1.420290e+02
## 1121 Blue 8.110110e+01 19.585436620 7.206122e+01 7.751020e+01 8.260204e+01
## 1122 GLI 1.834788e-01 0.028294104 1.819230e-01 1.898909e-01 1.964345e-01
## 1123 gray 1.522604e+02 14.764932821 1.445142e+02 1.507554e+02 1.569166e+02
## 1124 Green 1.669542e+02 13.990913450 1.595000e+02 1.660816e+02 1.721633e+02
## 1125 HI 6.180147e-01 0.050472190 5.743553e-01 6.335472e-01 6.470588e-01
## 1126 HUE -1.566654e+00 0.001409423 -1.567265e+00 -1.566921e+00 -1.566494e+00
## 1127 NGRDI 5.238409e-02 0.010588912 4.844471e-02 5.247905e-02 6.001343e-02
## 1128 Red 1.505443e+02 15.296378341 1.422653e+02 1.485510e+02 1.554898e+02
## 1129 SCI -5.238409e-02 0.010588912 -6.001343e-02 -5.247905e-02 -4.844471e-02
## 1130 SI 3.065535e-01 0.051767056 2.973581e-01 3.166563e-01 3.327070e-01
## 1131 VARI 6.974471e-02 0.013018130 6.419647e-02 6.901146e-02 7.995474e-02
## 1132 BGI 5.181075e-01 0.068363670 4.873843e-01 5.059568e-01 5.272023e-01
## 1133 BI 1.163349e+02 16.070112835 1.088619e+02 1.140037e+02 1.194544e+02
## 1134 Blue 7.468722e+01 18.778085672 6.746939e+01 7.146939e+01 7.546939e+01
## 1135 GLI 1.914194e-01 0.030226781 1.887478e-01 1.971280e-01 2.047002e-01
## 1136 gray 1.283932e+02 15.739239594 1.206865e+02 1.262927e+02 1.322926e+02
## 1137 Green 1.428105e+02 14.813464029 1.352245e+02 1.411224e+02 1.472449e+02
## 1138 HI 3.431670e-01 0.096389971 2.647059e-01 3.538462e-01 4.052184e-01
## 1139 HUE -1.566987e+00 0.001271632 -1.567413e+00 -1.567244e+00 -1.566957e+00
## 1140 NGRDI 8.631994e-02 0.017657830 8.039976e-02 8.675018e-02 9.857390e-02
## 1141 Red 1.205654e+02 17.177782156 1.116122e+02 1.176939e+02 1.251224e+02
## 1142 SCI -8.631994e-02 0.017657830 -9.857390e-02 -8.675018e-02 -8.039976e-02
## 1143 SI 2.404711e-01 0.044615347 2.236065e-01 2.449146e-01 2.644985e-01
## 1144 VARI 1.195189e-01 0.023396586 1.102392e-01 1.186167e-01 1.375628e-01
## 1145 BGI 4.977048e-01 0.071900823 4.658040e-01 4.803104e-01 5.011048e-01
## 1146 BI 1.500779e+02 13.501297581 1.426232e+02 1.483246e+02 1.558315e+02
## 1147 Blue 8.978499e+01 19.156292216 8.053061e+01 8.559184e+01 9.301531e+01
## 1148 GLI 1.707078e-01 0.027092189 1.662892e-01 1.768374e-01 1.846372e-01
## 1149 gray 1.647727e+02 13.013847383 1.573909e+02 1.634205e+02 1.710752e+02
## 1150 Green 1.791654e+02 12.274127833 1.720816e+02 1.782245e+02 1.857755e+02
## 1151 HI 6.850856e-01 0.068881595 6.386950e-01 6.750554e-01 6.842105e-01
## 1152 HUE -1.564609e+00 0.059978148 -1.567163e+00 -1.566854e+00 -1.566082e+00
## 1153 NGRDI 4.124948e-02 0.011068541 3.834929e-02 4.424936e-02 4.884655e-02
## 1154 Red 1.651073e+02 13.317742650 1.571020e+02 1.638878e+02 1.718776e+02
## 1155 SCI -4.124948e-02 0.011068541 -4.884655e-02 -4.424936e-02 -3.834929e-02
## 1156 SI 3.012645e-01 0.052454473 2.927425e-01 3.124210e-01 3.277086e-01
## 1157 VARI 5.540018e-02 0.014143176 5.204514e-02 5.907512e-02 6.517622e-02
## 1158 BGI 5.253363e-01 0.067629035 4.936205e-01 5.123868e-01 5.341050e-01
## 1159 BI 1.611653e+02 13.578761491 1.538553e+02 1.605841e+02 1.673491e+02
## 1160 Blue 9.921314e+01 19.003533597 8.944898e+01 9.644898e+01 1.039592e+02
## 1161 GLI 1.471165e-01 0.024127075 1.394103e-01 1.508226e-01 1.603053e-01
## 1162 gray 1.755543e+02 13.299696296 1.682493e+02 1.753822e+02 1.821610e+02
## 1163 Green 1.877050e+02 12.918404180 1.806735e+02 1.877551e+02 1.946735e+02
## 1164 HI 8.432663e-01 0.116994830 7.273714e-01 8.486218e-01 9.324234e-01
## 1165 HUE -1.256189e+00 0.890806511 -1.565558e+00 -1.562681e+00 -1.552513e+00
## 1166 NGRDI 1.891074e-02 0.014359333 8.185311e-03 1.891443e-02 3.036426e-02
## 1167 Red 1.808066e+02 13.434965099 1.733469e+02 1.805510e+02 1.878980e+02
## 1168 SCI -1.891074e-02 0.014359333 -3.036426e-02 -1.891443e-02 -8.185311e-03
## 1169 SI 2.964117e-01 0.053092933 2.850698e-01 3.045847e-01 3.243282e-01
## 1170 VARI 2.584586e-02 0.019483439 1.112625e-02 2.565829e-02 4.159864e-02
## 1171 BGI 4.909821e-01 0.076433300 4.546185e-01 4.741245e-01 4.946815e-01
## 1172 BI 1.316202e+02 16.319855797 1.219246e+02 1.285912e+02 1.369749e+02
## 1173 Blue 7.893557e+01 20.421924398 6.875510e+01 7.459184e+01 8.120408e+01
## 1174 GLI 1.828602e-01 0.031902343 1.774208e-01 1.898081e-01 1.998493e-01
## 1175 gray 1.449903e+02 15.978123021 1.352068e+02 1.422197e+02 1.511551e+02
## 1176 Green 1.591347e+02 15.143668444 1.496071e+02 1.568367e+02 1.657755e+02
## 1177 HI 5.835302e-01 0.087230938 5.233495e-01 5.755921e-01 6.446344e-01
## 1178 HUE -1.565185e+00 0.056844131 -1.567252e+00 -1.566854e+00 -1.566054e+00
## 1179 NGRDI 5.660431e-02 0.016043690 4.637541e-02 5.825661e-02 6.869171e-02
## 1180 Red 1.424065e+02 17.029333387 1.319592e+02 1.393980e+02 1.489796e+02
## 1181 SCI -5.660431e-02 0.016043690 -6.869171e-02 -5.825661e-02 -4.637541e-02
## 1182 SI 2.940035e-01 0.052286143 2.846082e-01 3.037771e-01 3.223045e-01
## 1183 VARI 7.584273e-02 0.020423375 6.218717e-02 7.792345e-02 9.096605e-02
## 1184 BGI 4.736437e-01 0.072212077 4.361223e-01 4.614656e-01 4.888106e-01
## 1185 BI 1.446258e+02 14.468062144 1.359823e+02 1.436407e+02 1.511702e+02
## 1186 Blue 8.342288e+01 19.322375240 7.302041e+01 8.069388e+01 8.867857e+01
## 1187 GLI 1.833963e-01 0.028569623 1.757036e-01 1.884002e-01 1.992038e-01
## 1188 gray 1.594152e+02 14.231637535 1.507813e+02 1.588291e+02 1.666201e+02
## 1189 Green 1.745284e+02 13.460947280 1.664031e+02 1.743980e+02 1.819184e+02
## 1190 HI 6.532066e-01 0.041206364 6.379420e-01 6.459620e-01 6.774194e-01
## 1191 HUE -1.566672e+00 0.005955030 -1.567267e+00 -1.567020e+00 -1.566662e+00
## 1192 NGRDI 4.800437e-02 0.009296472 4.393461e-02 4.901220e-02 5.308099e-02
## 1193 Red 1.587183e+02 14.569975564 1.494898e+02 1.578980e+02 1.662245e+02
## 1194 SCI -4.800437e-02 0.009296472 -5.308099e-02 -4.901220e-02 -4.393461e-02
## 1195 SI 3.179923e-01 0.055246255 3.010899e-01 3.238763e-01 3.483207e-01
## 1196 VARI 6.348123e-02 0.010905239 5.866035e-02 6.449864e-02 6.898476e-02
## 1197 BGI 5.509069e-01 0.065439595 5.162548e-01 5.366734e-01 5.652501e-01
## 1198 BI 1.575559e+02 14.935285964 1.493091e+02 1.580308e+02 1.656656e+02
## 1199 Blue 9.839235e+01 19.698069087 8.722959e+01 9.608163e+01 1.049745e+02
## 1200 GLI 1.175620e-01 0.021738963 1.072494e-01 1.197379e-01 1.317572e-01
## 1201 gray 1.697889e+02 15.520961712 1.611838e+02 1.708662e+02 1.792393e+02
## 1202 Green 1.774054e+02 17.056276178 1.672908e+02 1.789796e+02 1.888980e+02
## 1203 HI 1.127089e+00 0.213384041 9.629172e-01 1.138308e+00 1.271128e+00
## 1204 HUE 6.361729e-01 1.410905041 -1.534243e+00 1.560143e+00 1.565094e+00
## 1205 NGRDI -1.408290e-02 0.023231293 -3.004887e-02 -1.500344e-02 4.101386e-03
## 1206 Red 1.820576e+02 12.774441026 1.753316e+02 1.827347e+02 1.891837e+02
## 1207 SCI 1.408290e-02 0.023231293 -4.101386e-03 1.500344e-02 3.004887e-02
## 1208 SI 3.041629e-01 0.058549704 2.835034e-01 3.125746e-01 3.388524e-01
## 1209 VARI -1.875302e-02 0.031458960 -4.064677e-02 -2.066285e-02 5.830924e-03
## 1210 BGI 4.603501e-01 0.066690415 4.293498e-01 4.464923e-01 4.688840e-01
## 1211 BI 1.369996e+02 12.903926346 1.303857e+02 1.353736e+02 1.406537e+02
## 1212 Blue 7.717352e+01 17.128535412 6.948980e+01 7.432653e+01 7.971429e+01
## 1213 GLI 1.907362e-01 0.027658058 1.854010e-01 1.970940e-01 2.050209e-01
## 1214 gray 1.513755e+02 12.533123287 1.447472e+02 1.500102e+02 1.555212e+02
## 1215 Green 1.664320e+02 11.542724972 1.603265e+02 1.656122e+02 1.709388e+02
## 1216 HI 6.338923e-01 0.090799056 5.515404e-01 6.348096e-01 6.772152e-01
## 1217 HUE -1.548845e+00 0.229626368 -1.567450e+00 -1.567049e+00 -1.566466e+00
## 1218 NGRDI 5.237126e-02 0.014917541 4.633257e-02 5.440053e-02 6.384365e-02
## 1219 Red 1.501077e+02 13.853210365 1.422857e+02 1.477755e+02 1.547959e+02
## 1220 SCI -5.237126e-02 0.014917541 -6.384365e-02 -5.440053e-02 -4.633257e-02
## 1221 SI 3.263082e-01 0.050926985 3.113899e-01 3.341239e-01 3.550033e-01
## 1222 VARI 6.881538e-02 0.019124398 6.061852e-02 7.061930e-02 8.483172e-02
## 1223 BGI 4.945712e-01 0.072534243 4.608673e-01 4.801561e-01 5.029688e-01
## 1224 BI 1.355914e+02 15.327820824 1.282724e+02 1.341147e+02 1.408786e+02
## 1225 Blue 8.048875e+01 19.806457644 7.151020e+01 7.730612e+01 8.369388e+01
## 1226 GLI 1.677679e-01 0.028555953 1.594224e-01 1.741036e-01 1.847201e-01
## 1227 gray 1.486683e+02 15.112120934 1.414012e+02 1.476821e+02 1.546591e+02
## 1228 Green 1.610853e+02 14.800418939 1.540408e+02 1.610612e+02 1.678367e+02
## 1229 HI 7.347673e-01 0.140129855 6.384710e-01 6.915866e-01 8.122867e-01
## 1230 HUE -1.383908e+00 0.711218484 -1.566389e+00 -1.565415e+00 -1.561489e+00
## 1231 NGRDI 3.506482e-02 0.019472248 2.233800e-02 3.980236e-02 4.909028e-02
## 1232 Red 1.502860e+02 15.355172926 1.422245e+02 1.482857e+02 1.566327e+02
## 1233 SCI -3.506482e-02 0.019472248 -4.909028e-02 -3.980236e-02 -2.233800e-02
## 1234 SI 3.096930e-01 0.056438616 2.932626e-01 3.152297e-01 3.387922e-01
## 1235 VARI 4.701988e-02 0.025845249 3.067382e-02 5.345882e-02 6.551386e-02
## 1236 BGI 4.833572e-01 0.076549012 4.456626e-01 4.703528e-01 4.968566e-01
## 1237 BI 1.332715e+02 16.075478052 1.247668e+02 1.311760e+02 1.386993e+02
## 1238 Blue 7.767911e+01 20.781556650 6.732653e+01 7.412245e+01 8.144898e+01
## 1239 GLI 1.719859e-01 0.029334902 1.630791e-01 1.766150e-01 1.883165e-01
## 1240 gray 1.462727e+02 15.829454682 1.378049e+02 1.446074e+02 1.523001e+02
## 1241 Green 1.587213e+02 15.267577895 1.504490e+02 1.576327e+02 1.653265e+02
## 1242 HI 7.344640e-01 0.141404219 6.357214e-01 7.025882e-01 8.295425e-01
## 1243 HUE -1.397312e+00 0.682988251 -1.566325e+00 -1.565095e+00 -1.560846e+00
## 1244 NGRDI 3.562392e-02 0.019778881 2.172519e-02 3.921069e-02 4.992249e-02
## 1245 Red 1.479862e+02 16.363214380 1.385918e+02 1.457551e+02 1.546327e+02
## 1246 SCI -3.562392e-02 0.019778881 -4.992249e-02 -3.921069e-02 -2.172519e-02
## 1247 SI 3.199480e-01 0.062886283 2.954565e-01 3.264114e-01 3.563706e-01
## 1248 VARI 4.751978e-02 0.026222598 2.910383e-02 5.226047e-02 6.626938e-02
## 1249 BGI 4.891723e-01 0.077124038 4.502185e-01 4.718030e-01 4.980867e-01
## 1250 BI 1.511301e+02 15.085195539 1.423862e+02 1.496438e+02 1.573360e+02
## 1251 Blue 8.899860e+01 21.120474811 7.759184e+01 8.475510e+01 9.359184e+01
## 1252 GLI 1.718740e-01 0.028383481 1.672119e-01 1.778123e-01 1.866563e-01
## 1253 gray 1.659428e+02 14.691293244 1.572901e+02 1.649439e+02 1.727401e+02
## 1254 Green 1.802973e+02 14.015851600 1.719796e+02 1.797551e+02 1.876735e+02
## 1255 HI 7.107380e-01 0.049556763 6.790805e-01 6.962406e-01 7.156105e-01
## 1256 HUE -1.565891e+00 0.002488825 -1.566880e+00 -1.566620e+00 -1.565975e+00
## 1257 NGRDI 3.833134e-02 0.008556742 3.585884e-02 4.100876e-02 4.359573e-02
## 1258 Red 1.670983e+02 14.538445320 1.586327e+02 1.662041e+02 1.738980e+02
## 1259 SCI -3.833134e-02 0.008556742 -4.359573e-02 -4.100876e-02 -3.585884e-02
## 1260 SI 3.123070e-01 0.057964673 2.994342e-01 3.240924e-01 3.440454e-01
## 1261 VARI 5.109758e-02 0.010514036 4.851043e-02 5.457916e-02 5.762141e-02
## 1262 BGI 5.497641e-01 0.071990678 5.051916e-01 5.345074e-01 5.746432e-01
## 1263 BI 1.592227e+02 15.343947347 1.515961e+02 1.589253e+02 1.661636e+02
## 1264 Blue 9.815995e+01 20.578510863 8.644898e+01 9.457143e+01 1.044694e+02
## 1265 GLI 1.106292e-01 0.025172084 1.020395e-01 1.158511e-01 1.265604e-01
## 1266 gray 1.710800e+02 15.796697661 1.636931e+02 1.713225e+02 1.786709e+02
## 1267 Green 1.774539e+02 16.914863703 1.702653e+02 1.781837e+02 1.857143e+02
## 1268 HI 1.240187e+00 0.268503292 1.141919e+00 1.215232e+00 1.304976e+00
## 1269 HUE 1.223562e+00 0.964983504 1.561161e+00 1.564360e+00 1.566215e+00
## 1270 NGRDI -2.556116e-02 0.023643060 -3.449972e-02 -2.424276e-02 -1.622224e-02
## 1271 Red 1.863689e+02 13.917131854 1.789592e+02 1.869184e+02 1.942245e+02
## 1272 SCI 2.556116e-02 0.023643060 1.622224e-02 2.424276e-02 3.449972e-02
## 1273 SI 3.160340e-01 0.061573889 2.956308e-01 3.276614e-01 3.538531e-01
## 1274 VARI -3.446801e-02 0.032521555 -4.624071e-02 -3.279446e-02 -2.202713e-02
## 1275 BGI 5.551954e-01 0.060746322 5.231954e-01 5.460024e-01 5.712243e-01
## 1276 BI 1.661646e+02 14.931612821 1.569303e+02 1.664568e+02 1.747483e+02
## 1277 Blue 1.042867e+02 19.142136682 9.310204e+01 1.024898e+02 1.119184e+02
## 1278 GLI 1.156305e-01 0.019603682 1.054500e-01 1.165262e-01 1.280371e-01
## 1279 gray 1.789397e+02 15.175842212 1.695171e+02 1.797403e+02 1.882576e+02
## 1280 Green 1.866665e+02 15.709198703 1.768163e+02 1.877347e+02 1.970918e+02
## 1281 HI 1.136177e+00 0.179695391 1.006772e+00 1.152008e+00 1.262136e+00
## 1282 HUE 8.152243e-01 1.315047653 1.375514e+00 1.561724e+00 1.565548e+00
## 1283 NGRDI -1.503247e-02 0.019851187 -2.870132e-02 -1.640505e-02 -7.652383e-04
## 1284 Red 1.922333e+02 14.629418358 1.832245e+02 1.923265e+02 2.014898e+02
## 1285 SCI 1.503247e-02 0.019851187 7.652383e-04 1.640505e-02 2.870132e-02
## 1286 SI 3.012859e-01 0.053649145 2.808378e-01 3.067718e-01 3.314434e-01
## 1287 VARI -2.023670e-02 0.026859109 -3.897291e-02 -2.256197e-02 -1.041628e-03
## 1288 BGI 5.782218e-01 0.073713618 5.328897e-01 5.679634e-01 6.076278e-01
## 1289 BI 1.531340e+02 20.303029478 1.416983e+02 1.542746e+02 1.660485e+02
## 1290 Blue 9.713375e+01 22.574468320 8.230612e+01 9.493878e+01 1.087143e+02
## 1291 GLI 8.987709e-02 0.031091971 7.255340e-02 9.339408e-02 1.115092e-01
## 1292 gray 1.632016e+02 21.773797084 1.513917e+02 1.650409e+02 1.775392e+02
## 1293 Green 1.668868e+02 24.228789246 1.538980e+02 1.691633e+02 1.834592e+02
## 1294 HI 1.458850e+00 0.447086283 1.158589e+00 1.417849e+00 1.683580e+00
## 1295 HUE 1.107249e+00 1.096934808 1.560294e+00 1.566651e+00 1.568067e+00
## 1296 NGRDI -4.370844e-02 0.039046882 -6.815533e-02 -4.256342e-02 -1.584418e-02
## 1297 Red 1.811563e+02 19.653342033 1.704694e+02 1.823878e+02 1.941020e+02
## 1298 SCI 4.370844e-02 0.039046882 1.584418e-02 4.256342e-02 6.815533e-02
## 1299 SI 3.092505e-01 0.068714780 2.721891e-01 3.173554e-01 3.538793e-01
## 1300 VARI -5.934955e-02 0.052935406 -9.209065e-02 -5.845817e-02 -2.228588e-02
## 1301 BGI 4.578249e-01 0.069788671 4.253275e-01 4.455446e-01 4.685013e-01
## 1302 BI 1.394011e+02 13.902080948 1.307526e+02 1.376127e+02 1.459149e+02
## 1303 Blue 7.836742e+01 18.221380826 6.916327e+01 7.551020e+01 8.267347e+01
## 1304 GLI 1.932495e-01 0.028192742 1.870605e-01 1.981824e-01 2.075384e-01
## 1305 gray 1.541359e+02 13.670509813 1.453141e+02 1.525849e+02 1.613091e+02
## 1306 Green 1.697594e+02 12.891990425 1.612857e+02 1.686939e+02 1.772041e+02
## 1307 HI 6.182237e-01 0.057107968 5.581872e-01 6.381407e-01 6.518657e-01
## 1308 HUE -1.567001e+00 0.002008998 -1.567533e+00 -1.567223e+00 -1.566934e+00
## 1309 NGRDI 5.476766e-02 0.011487292 4.871060e-02 5.427046e-02 6.345700e-02
## 1310 Red 1.523520e+02 14.447575699 1.427551e+02 1.505102e+02 1.602857e+02
## 1311 SCI -5.476766e-02 0.011487292 -6.345700e-02 -5.427046e-02 -4.871060e-02
## 1312 SI 3.269492e-01 0.052396085 3.131143e-01 3.343954e-01 3.537519e-01
## 1313 VARI 7.183425e-02 0.014044639 6.398123e-02 7.022425e-02 8.389617e-02
## 1314 BGI 5.433003e-01 0.080509769 5.003178e-01 5.260650e-01 5.603382e-01
## 1315 BI 1.229925e+02 22.676941957 1.083390e+02 1.180642e+02 1.339646e+02
## 1316 Blue 7.443487e+01 24.700586500 6.047449e+01 6.772449e+01 7.991837e+01
## 1317 GLI 1.002194e-01 0.028803679 8.636963e-02 1.018091e-01 1.184094e-01
## 1318 gray 1.315358e+02 24.166978328 1.156010e+02 1.263858e+02 1.441172e+02
## 1319 Green 1.351380e+02 26.769994709 1.171224e+02 1.293980e+02 1.495714e+02
## 1320 HI 1.412909e+00 0.343236318 1.213558e+00 1.441452e+00 1.625032e+00
## 1321 HUE 1.141374e+00 1.061235025 1.560271e+00 1.565005e+00 1.566374e+00
## 1322 NGRDI -4.408670e-02 0.034088184 -6.872650e-02 -4.979960e-02 -2.280517e-02
## 1323 Red 1.462348e+02 20.249014525 1.331224e+02 1.430408e+02 1.577347e+02
## 1324 SCI 4.408670e-02 0.034088184 2.280517e-02 4.979960e-02 6.872650e-02
## 1325 SI 3.380381e-01 0.073130513 3.153315e-01 3.549401e-01 3.811566e-01
## 1326 VARI -5.851747e-02 0.045732819 -9.082683e-02 -6.616031e-02 -3.099298e-02
## 1327 BGI 4.950562e-01 0.075654157 4.563590e-01 4.833557e-01 5.115477e-01
## 1328 BI 1.306872e+02 17.507306667 1.202224e+02 1.289388e+02 1.384521e+02
## 1329 Blue 7.607665e+01 21.258945133 6.471429e+01 7.250000e+01 8.136735e+01
## 1330 GLI 1.493519e-01 0.031952196 1.302149e-01 1.524699e-01 1.723624e-01
## 1331 gray 1.423360e+02 18.051337428 1.308762e+02 1.407138e+02 1.516943e+02
## 1332 Green 1.516844e+02 19.328922610 1.383418e+02 1.501020e+02 1.637806e+02
## 1333 HI 9.545467e-01 0.229851933 7.446127e-01 9.392129e-01 1.141102e+00
## 1334 HUE -2.372749e-01 1.524993615 -1.564403e+00 -1.540947e+00 1.556133e+00
## 1335 NGRDI 6.915608e-03 0.029070267 -1.673489e-02 7.660257e-03 3.204692e-02
## 1336 Red 1.492458e+02 16.165977805 1.402449e+02 1.480408e+02 1.563724e+02
## 1337 SCI -6.915608e-03 0.029070267 -3.204692e-02 -7.660257e-03 1.673489e-02
## 1338 SI 3.345940e-01 0.063954079 3.116501e-01 3.420964e-01 3.707661e-01
## 1339 VARI 9.392848e-03 0.038543776 -2.224237e-02 1.030962e-02 4.327312e-02
## 1340 BGI 4.825232e-01 0.064109709 4.487681e-01 4.695543e-01 4.968354e-01
## 1341 BI 1.589125e+02 14.604421080 1.490106e+02 1.565937e+02 1.667296e+02
## 1342 Blue 9.097271e+01 17.795859455 8.085714e+01 8.736735e+01 9.642857e+01
## 1343 GLI 1.638030e-01 0.024559347 1.539416e-01 1.678815e-01 1.793575e-01
## 1344 gray 1.740549e+02 14.628133723 1.640534e+02 1.719860e+02 1.822361e+02
## 1345 Green 1.874722e+02 13.953248417 1.780204e+02 1.859388e+02 1.955306e+02
## 1346 HI 8.278611e-01 0.142537201 7.082585e-01 8.025809e-01 9.149619e-01
## 1347 HUE -1.132381e+00 1.066448924 -1.566595e+00 -1.564801e+00 -1.557562e+00
## 1348 NGRDI 2.281530e-02 0.019125184 1.083953e-02 2.537253e-02 3.903968e-02
## 1349 Red 1.793906e+02 16.886961273 1.672857e+02 1.768980e+02 1.891429e+02
## 1350 SCI -2.281530e-02 0.019125184 -3.903968e-02 -2.537253e-02 -1.083953e-02
## 1351 SI 3.311142e-01 0.054800879 3.115736e-01 3.378747e-01 3.635422e-01
## 1352 VARI 3.042245e-02 0.025250191 1.462237e-02 3.390225e-02 5.215949e-02
## 1353 BGI 5.079571e-01 0.069477455 4.733950e-01 4.951851e-01 5.198283e-01
## 1354 BI 1.443206e+02 15.621134529 1.345758e+02 1.435821e+02 1.523447e+02
## 1355 Blue 8.561669e+01 19.784450282 7.467347e+01 8.279592e+01 9.114286e+01
## 1356 GLI 1.450213e-01 0.025913335 1.331806e-01 1.481585e-01 1.618094e-01
## 1357 gray 1.570285e+02 15.900702841 1.469231e+02 1.567712e+02 1.661186e+02
## 1358 Green 1.670401e+02 16.587756186 1.564082e+02 1.673265e+02 1.774592e+02
## 1359 HI 9.460817e-01 0.183930315 7.989478e-01 9.359734e-01 1.084247e+00
## 1360 HUE -3.656883e-01 1.497186552 -1.563539e+00 -1.547094e+00 1.551803e+00
## 1361 NGRDI 6.913620e-03 0.022677370 -9.986760e-03 7.851718e-03 2.507888e-02
## 1362 Red 1.646008e+02 14.992481853 1.548980e+02 1.640612e+02 1.731837e+02
## 1363 SCI -6.913620e-03 0.022677370 -2.507888e-02 -7.851718e-03 9.986760e-03
## 1364 SI 3.224781e-01 0.058898945 3.040703e-01 3.306876e-01 3.551841e-01
## 1365 VARI 9.564455e-03 0.030336141 -1.343971e-02 1.055493e-02 3.411655e-02
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## 1088 63 49.8000000 49.86 48.71 49.8000000
## 1089 63 49.8000000 49.86 48.71 49.8000000
## 1090 63 49.8000000 49.86 48.71 49.8000000
## 1091 63 49.8000000 49.86 48.71 49.8000000
## 1092 63 49.8000000 49.86 48.71 49.8000000
## 1093 64 11.6500000 12.31 10.33 11.6500000
## 1094 64 11.6500000 12.31 10.33 11.6500000
## 1095 64 11.6500000 12.31 10.33 11.6500000
## 1096 64 11.6500000 12.31 10.33 11.6500000
## 1097 64 11.6500000 12.31 10.33 11.6500000
## 1098 64 11.6500000 12.31 10.33 11.6500000
## 1099 64 11.6500000 12.31 10.33 11.6500000
## 1100 64 11.6500000 12.31 10.33 11.6500000
## 1101 64 11.6500000 12.31 10.33 11.6500000
## 1102 64 11.6500000 12.31 10.33 11.6500000
## 1103 64 11.6500000 12.31 10.33 11.6500000
## 1104 64 11.6500000 12.31 10.33 11.6500000
## 1105 64 11.6500000 12.31 10.33 11.6500000
## 1106 65 18.3600000 18.62 16.06 18.3600000
## 1107 65 18.3600000 18.62 16.06 18.3600000
## 1108 65 18.3600000 18.62 16.06 18.3600000
## 1109 65 18.3600000 18.62 16.06 18.3600000
## 1110 65 18.3600000 18.62 16.06 18.3600000
## 1111 65 18.3600000 18.62 16.06 18.3600000
## 1112 65 18.3600000 18.62 16.06 18.3600000
## 1113 65 18.3600000 18.62 16.06 18.3600000
## 1114 65 18.3600000 18.62 16.06 18.3600000
## 1115 65 18.3600000 18.62 16.06 18.3600000
## 1116 65 18.3600000 18.62 16.06 18.3600000
## 1117 65 18.3600000 18.62 16.06 18.3600000
## 1118 65 18.3600000 18.62 16.06 18.3600000
## 1119 68 2.6400000 2.55 3.38 2.6400000
## 1120 68 2.6400000 2.55 3.38 2.6400000
## 1121 68 2.6400000 2.55 3.38 2.6400000
## 1122 68 2.6400000 2.55 3.38 2.6400000
## 1123 68 2.6400000 2.55 3.38 2.6400000
## 1124 68 2.6400000 2.55 3.38 2.6400000
## 1125 68 2.6400000 2.55 3.38 2.6400000
## 1126 68 2.6400000 2.55 3.38 2.6400000
## 1127 68 2.6400000 2.55 3.38 2.6400000
## 1128 68 2.6400000 2.55 3.38 2.6400000
## 1129 68 2.6400000 2.55 3.38 2.6400000
## 1130 68 2.6400000 2.55 3.38 2.6400000
## 1131 68 2.6400000 2.55 3.38 2.6400000
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## 1133 7 3.7800000 3.30 3.11 3.7800000
## 1134 7 3.7800000 3.30 3.11 3.7800000
## 1135 7 3.7800000 3.30 3.11 3.7800000
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## 1137 7 3.7800000 3.30 3.11 3.7800000
## 1138 7 3.7800000 3.30 3.11 3.7800000
## 1139 7 3.7800000 3.30 3.11 3.7800000
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## 1141 7 3.7800000 3.30 3.11 3.7800000
## 1142 7 3.7800000 3.30 3.11 3.7800000
## 1143 7 3.7800000 3.30 3.11 3.7800000
## 1144 7 3.7800000 3.30 3.11 3.7800000
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## 1146 70 11.7900000 12.52 13.70 11.7900000
## 1147 70 11.7900000 12.52 13.70 11.7900000
## 1148 70 11.7900000 12.52 13.70 11.7900000
## 1149 70 11.7900000 12.52 13.70 11.7900000
## 1150 70 11.7900000 12.52 13.70 11.7900000
## 1151 70 11.7900000 12.52 13.70 11.7900000
## 1152 70 11.7900000 12.52 13.70 11.7900000
## 1153 70 11.7900000 12.52 13.70 11.7900000
## 1154 70 11.7900000 12.52 13.70 11.7900000
## 1155 70 11.7900000 12.52 13.70 11.7900000
## 1156 70 11.7900000 12.52 13.70 11.7900000
## 1157 70 11.7900000 12.52 13.70 11.7900000
## 1158 72 37.8300000 38.33 36.45 37.8300000
## 1159 72 37.8300000 38.33 36.45 37.8300000
## 1160 72 37.8300000 38.33 36.45 37.8300000
## 1161 72 37.8300000 38.33 36.45 37.8300000
## 1162 72 37.8300000 38.33 36.45 37.8300000
## 1163 72 37.8300000 38.33 36.45 37.8300000
## 1164 72 37.8300000 38.33 36.45 37.8300000
## 1165 72 37.8300000 38.33 36.45 37.8300000
## 1166 72 37.8300000 38.33 36.45 37.8300000
## 1167 72 37.8300000 38.33 36.45 37.8300000
## 1168 72 37.8300000 38.33 36.45 37.8300000
## 1169 72 37.8300000 38.33 36.45 37.8300000
## 1170 72 37.8300000 38.33 36.45 37.8300000
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## 1172 73 38.4500000 39.29 35.31 38.4500000
## 1173 73 38.4500000 39.29 35.31 38.4500000
## 1174 73 38.4500000 39.29 35.31 38.4500000
## 1175 73 38.4500000 39.29 35.31 38.4500000
## 1176 73 38.4500000 39.29 35.31 38.4500000
## 1177 73 38.4500000 39.29 35.31 38.4500000
## 1178 73 38.4500000 39.29 35.31 38.4500000
## 1179 73 38.4500000 39.29 35.31 38.4500000
## 1180 73 38.4500000 39.29 35.31 38.4500000
## 1181 73 38.4500000 39.29 35.31 38.4500000
## 1182 73 38.4500000 39.29 35.31 38.4500000
## 1183 73 38.4500000 39.29 35.31 38.4500000
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## 1185 74 1.2600000 1.37 1.76 1.2600000
## 1186 74 1.2600000 1.37 1.76 1.2600000
## 1187 74 1.2600000 1.37 1.76 1.2600000
## 1188 74 1.2600000 1.37 1.76 1.2600000
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## 1190 74 1.2600000 1.37 1.76 1.2600000
## 1191 74 1.2600000 1.37 1.76 1.2600000
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## 1194 74 1.2600000 1.37 1.76 1.2600000
## 1195 74 1.2600000 1.37 1.76 1.2600000
## 1196 74 1.2600000 1.37 1.76 1.2600000
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## 1198 75 71.5100000 72.21 68.56 71.5100000
## 1199 75 71.5100000 72.21 68.56 71.5100000
## 1200 75 71.5100000 72.21 68.56 71.5100000
## 1201 75 71.5100000 72.21 68.56 71.5100000
## 1202 75 71.5100000 72.21 68.56 71.5100000
## 1203 75 71.5100000 72.21 68.56 71.5100000
## 1204 75 71.5100000 72.21 68.56 71.5100000
## 1205 75 71.5100000 72.21 68.56 71.5100000
## 1206 75 71.5100000 72.21 68.56 71.5100000
## 1207 75 71.5100000 72.21 68.56 71.5100000
## 1208 75 71.5100000 72.21 68.56 71.5100000
## 1209 75 71.5100000 72.21 68.56 71.5100000
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## 1211 76 10.1100000 11.60 8.80 10.1100000
## 1212 76 10.1100000 11.60 8.80 10.1100000
## 1213 76 10.1100000 11.60 8.80 10.1100000
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## 1278 81 63.9100000 64.62 67.52 63.9100000
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## 1280 81 63.9100000 64.62 67.52 63.9100000
## 1281 81 63.9100000 64.62 67.52 63.9100000
## 1282 81 63.9100000 64.62 67.52 63.9100000
## 1283 81 63.9100000 64.62 67.52 63.9100000
## 1284 81 63.9100000 64.62 67.52 63.9100000
## 1285 81 63.9100000 64.62 67.52 63.9100000
## 1286 81 63.9100000 64.62 67.52 63.9100000
## 1287 81 63.9100000 64.62 67.52 63.9100000
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## 1290 82 75.8200000 74.19 76.76 75.8200000
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## 1351 87 42.8400000 41.87 44.72 42.8400000
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## 1353 88 59.0100000 58.44 57.79 59.0100000
## 1354 88 59.0100000 58.44 57.79 59.0100000
## 1355 88 59.0100000 58.44 57.79 59.0100000
## 1356 88 59.0100000 58.44 57.79 59.0100000
## 1357 88 59.0100000 58.44 57.79 59.0100000
## 1358 88 59.0100000 58.44 57.79 59.0100000
## 1359 88 59.0100000 58.44 57.79 59.0100000
## 1360 88 59.0100000 58.44 57.79 59.0100000
## 1361 88 59.0100000 58.44 57.79 59.0100000
## 1362 88 59.0100000 58.44 57.79 59.0100000
## 1363 88 59.0100000 58.44 57.79 59.0100000
## 1364 88 59.0100000 58.44 57.79 59.0100000
## 1365 88 59.0100000 58.44 57.79 59.0100000# length(unique(data_xy$sev))summary(data_xy$sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.42 8.50 19.07 25.20 37.83 83.99Images
hist_sev_xy = data_xy %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"))EX.L1<-stack(paste("./pics/01-Xylella-tobacco-bg-white/","75.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)## [1] "3 layers available"EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)## [1] "3 layers available"EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=200, cropAbove=T, plot = F)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)## [1] "3 layers available"# plot(EX.L4$HUE)rgb_fig_xy = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))## Coordinate system already present. Adding new coordinate system, which will replace the existing one.gli_fig_xy = as.data.frame(EX.L4$HUE, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = HUE))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
# rgb_fig_xy+ gli_fig_xy# rgb_fig_sbr + gli_fig_sbr + hist_sev_sbr #+
# rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
rgb_fig_xy + gli_fig_xy +hist_sev_xy## Warning: Removed 2 rows containing missing values (geom_bar).
# plot_layout(widths = c(1, 1, 1),
# heights = c(1,1,1))
#
# ggsave("figs/leaf_gli.png",dpi = 600, height = 7, width =8)Relationship sev indices
rgb_gg_xy = data_xy %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_xy## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_xy = data_xy %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_xy## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_xy,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_xy,
nrow = 2,
rel_heights = c(0.5,1))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_XY.png", dpi = 500, height = 8, width = 10)cor_xy = data_xy %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = round(cor.test(mean,sev, method = "spearman")$p.value,4)) %>%
arrange(-cor)## `summarise()` ungrouping output (override with `.groups` argument)cor_xy## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 HUE 0.694 0
## 2 HI 0.627 0
## 3 SCI 0.623 0
## 4 SI 0.356 0.0002
## 5 Red 0.286 0.0032
## 6 BI 0.219 0.0252
## 7 BGI 0.214 0.0288
## 8 Blue 0.195 0.0463
## 9 gray 0.194 0.0477
## 10 Green 0.121 0.217
## 11 GLI -0.49 0
## 12 NGRDI -0.623 0
## 13 VARI -0.624 0Spread df
all_data_spread = data_xy %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean)
all_data_spread## # A tibble: 105 x 15
## file sev BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 26.8 0.544 167. 106. 0.139 182. 193. 0.868 -1.23 0.0151 187.
## 2 10 24.3 0.523 162. 99.4 0.149 176. 189. 0.829 -1.31 0.0208 181.
## 3 11 16.3 0.477 135. 78.5 0.184 148. 163. 0.632 -1.57 0.0508 147.
## 4 12 14.8 0.547 163. 103. 0.138 177. 188. 0.863 -1.18 0.0158 182.
## 5 126 7.92 0.640 188. 135. 0.100 201. 210. 0.995 -0.634 0.00123 210.
## 6 127 9.48 0.582 177. 118. 0.125 191. 202. 0.900 -1.12 0.0124 198.
## 7 128 9.39 0.509 145. 87.0 0.157 158. 170. 0.794 -1.47 0.0260 161.
## 8 129 0.49 0.514 121. 76.6 0.192 133. 148. 0.360 -1.57 0.0845 125.
## 9 13 13.6 0.488 129. 77.1 0.183 143. 156. 0.599 -1.56 0.0546 140.
## 10 130 47.8 0.603 176. 118. 0.102 189. 196. 1.15 0.122 -0.0137 201.
## # ... with 95 more rows, and 3 more variables: SCI <dbl>, SI <dbl>, VARI <dbl>GBM
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 231.
## There were 13 predictors of which 13 had non-zero influence.sqrt(min(gbm.fit$cv.error))## [1] 12.43899gbm.perf(gbm.fit, method = "cv")
## [1] 231# find index for n trees with minimum CV errormin_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])## [1] 12.43899# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )## Using 231 trees...sqrt(mean(((pred)-all_data_spread$sev[-train])^2))## [1] 11.5175CCC((pred), all_data_spread$sev[-train])$rho.c$est## [1] 0.6909319plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Testing various hyperparameters
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)## [1] 192# randomize data
set.seed(123)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray +Red+Green+Blue + SI + SCI, #<<<<<
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC(pred, all_data_spread$sev[-train])$rho.c$est#<<<<<
}## Using 4918 trees...## Using 506 trees...## Using 53 trees...## Using 14 trees...## Using 2081 trees...## Using 191 trees...## Using 18 trees...## Using 13 trees...## Using 2081 trees...## Using 191 trees...## Using 19 trees...## Using 9 trees...## Using 2081 trees...## Using 191 trees...## Using 19 trees...## Using 9 trees...## Using 4991 trees...## Using 680 trees...## Using 53 trees...## Using 58 trees...## Using 4974 trees...## Using 530 trees...## Using 53 trees...## Using 60 trees...## Using 4974 trees...## Using 530 trees...## Using 53 trees...## Using 60 trees...## Using 4974 trees...## Using 530 trees...## Using 53 trees...## Using 60 trees...## Using 4986 trees...## Using 658 trees...## Using 89 trees...## Using 135 trees...## Using 4986 trees...## Using 658 trees...## Using 89 trees...## Using 135 trees...## Using 4986 trees...## Using 658 trees...## Using 89 trees...## Using 135 trees...## Using 4986 trees...## Using 658 trees...## Using 89 trees...## Using 135 trees...## Using 4990 trees...## Using 966 trees...## Using 64 trees...## Using 10 trees...## Using 1956 trees...## Using 170 trees...## Using 19 trees...
##
## Using 19 trees...## Using 1646 trees...## Using 166 trees...## Using 16 trees...## Using 4662 trees...## Using 1585 trees...## Using 166 trees...## Using 16 trees...## Using 25 trees...## Using 4748 trees...## Using 498 trees...## Using 54 trees...## Using 16 trees...## Using 2691 trees...## Using 279 trees...## Using 35 trees...## Using 9 trees...## Using 2691 trees...## Using 279 trees...## Using 35 trees...## Using 9 trees...## Using 2691 trees...## Using 279 trees...## Using 35 trees...## Using 9 trees...## Using 4987 trees...## Using 927 trees...## Using 96 trees...## Using 26 trees...## Using 4987 trees...## Using 927 trees...## Using 96 trees...## Using 26 trees...## Using 4987 trees...## Using 927 trees...## Using 96 trees...## Using 26 trees...## Using 4987 trees...## Using 927 trees...## Using 96 trees...## Using 26 trees...## Using 4969 trees...## Using 928 trees...## Using 52 trees...## Using 23 trees...## Using 1581 trees...## Using 152 trees...## Using 4993 trees...## Using 5 trees...## Using 1333 trees...## Using 96 trees...## Using 11 trees...## Using 4 trees...## Using 1312 trees...## Using 94 trees...## Using 11 trees...## Using 4 trees...## Using 4404 trees...## Using 380 trees...## Using 35 trees...## Using 13 trees...## Using 2265 trees...## Using 207 trees...## Using 25 trees...## Using 4 trees...## Using 2265 trees...## Using 207 trees...## Using 28 trees...## Using 4 trees...## Using 2265 trees...## Using 207 trees...## Using 28 trees...## Using 4 trees...## Using 4998 trees...## Using 863 trees...## Using 55 trees...## Using 30 trees...## Using 4608 trees...## Using 429 trees...## Using 56 trees...## Using 18 trees...## Using 4608 trees...## Using 429 trees...## Using 56 trees...## Using 18 trees...## Using 4608 trees...## Using 429 trees...## Using 56 trees...## Using 18 trees...## Using 5000 trees...## Using 2091 trees...## Using 218 trees...## Using 59 trees...## Using 2167 trees...## Using 203 trees...## Using 18 trees...## Using 3 trees...## Using 894 trees...## Using 88 trees...## Using 8 trees...## Using 2 trees...## Using 772 trees...## Using 74 trees...## Using 9 trees...## Using 2 trees...## Using 5000 trees...## Using 630 trees...## Using 63 trees...## Using 13 trees...## Using 2458 trees...## Using 250 trees...## Using 23 trees...## Using 6 trees...## Using 2392 trees...## Using 235 trees...## Using 21 trees...## Using 7 trees...## Using 2392 trees...## Using 235 trees...## Using 21 trees...## Using 7 trees...## Using 5000 trees...## Using 719 trees...## Using 76 trees...## Using 29 trees...## Using 3351 trees...## Using 331 trees...## Using 27 trees...## Using 6 trees...## Using 3351 trees...## Using 331 trees...## Using 27 trees...## Using 6 trees...## Using 3351 trees...## Using 331 trees...## Using 27 trees...## Using 6 trees...best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.30 3 5 0.65 19
## 2 0.30 5 5 0.65 4662
## 3 0.01 1 5 0.65 966
## 4 0.30 6 5 0.65 25
## 5 0.01 1 5 1.00 2091
## 6 0.01 1 5 0.80 928
## 7 0.30 1 5 0.80 23
## 8 0.30 1 5 1.00 59
## 9 0.10 1 5 1.00 218
## 10 0.10 1 5 0.65 64
## min_RMSE CCC
## 1 11.37018 0.8649715
## 2 12.21829 0.8407627
## 3 12.00979 0.8362572
## 4 12.57791 0.8356177
## 5 12.73349 0.8252694
## 6 12.61867 0.8244094
## 7 12.65166 0.8236509
## 8 12.65081 0.8226995
## 9 12.83123 0.8215836
## 10 12.07186 0.8157707# gbm.tune$fitBest model
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+Red+Green+Blue+gray+SI+SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)Relative influence
par(mar = c(5, 8, 1, 1))
summary_gbm_xy = summary(
gbm.fit.final,
cBars = 15,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_xy = summary_gbm_xy %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_xy
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "HUE", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( HUE,(yhat)))+
geom_line()
LIME
library(lime)model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
Prediction
# predict values for test data
pred <- predict(gbm.fit.final,
n.trees = gbm.fit.final$n.trees,
all_data_spread[-train,])
# results
caret::RMSE((pred), all_data_spread[-train,]$sev)## [1] 10.55157CCC((pred), all_data_spread$sev[-train])$rho.c$est## [1] 0.8649715cor((pred), all_data_spread$sev[-train])^2## [1] 0.757813accuracy_xy =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_xy## RMSE r s.shift l.shift C.b CCC CIS
## 1 10.55157 0.8705245 1.077005 0.08563874 0.9936211 0.8649715 0.7, 0.94plot
conc_xy = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)## `geom_smooth()` using formula 'y ~ x'Late blight
pics<-list.files("./pics/01-potato_late_bligh")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-potato_late_bligh/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=4)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
# EX1.Indices<- fieldIndex(mosaic = EX.L1,
# index = c("NGRDI","BGI","GLI", "SCI","HI", "SI"),
# myIndex = c("(Red-Blue)/Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex ="Red", cropValue=1, cropAbove=F, plot = F)
cut = mask(EX.L1, EX.L2$mask)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_PLB.txt")box = read.table("data/indexes_PLB.txt")Load severity data
library(gsheet)
sev_data = gsheet2tbl("https://docs.google.com/spreadsheets/d/1QMYhoFU4V4ItkAXyM_qYW4ODt0MctX9UJeFckB8bMQc/edit?usp=sharing")## Warning: Missing column names filled in: 'X9' [9]#
# sev_data %>%
# group_by(photo) %>%
# summarise(n())
length(unique(sev_data$photo))## [1] 200all_data_PI = box %>%
mutate(file = leaf) %>%
dplyr::select(-leaf) %>%
right_join(sev_data) #%>% ## Joining, by = "file" # filter(sev>0) %>%
# mutate(sev = case_when(sev==0 ~0.001,
# sev >0 ~sev))summary(all_data_PI$sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 37.24 49.47 47.63 63.78 92.38Images
hist_sev_pi = all_data_PI %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins =20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))EX.L1<-stack(paste("./pics/01-potato_late_bligh/","PI21_2D.png",sep = ""))
EX.L1<-aggregate(EX.L1, fact=10)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)## [1] "3 layers available"EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)## [1] "3 layers available"EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Red"), cropValue=1, cropAbove=F, plot = F)## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot = F)## [1] "3 layers available"# plot(EX.L4$HUE)rgb_fig_pi = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
xlim(250,1500)+
ylim(50,1300)+
theme(panel.background = element_rect(color = "white"))## Coordinate system already present. Adding new coordinate system, which will replace the existing one.gli_fig_pi = as.data.frame(EX.L4$BGI, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = BGI))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
xlim(250,1500)+
ylim(50,1300)+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
# rgb_fig_pi + gli_fig_pirgb_fig_sbr + gli_fig_sbr + hist_sev_sbr #+## Warning: Removed 2 rows containing missing values (geom_bar).
# rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
# rgb_fig_xy + gli_fig_xy +hist_sev_xy+
# rgb_fig_pi + gli_fig_pi +hist_sev_pi+
# plot_layout(widths = c(1, 1, 1),
# heights = c(1,1,1,1))+
# plot_annotation(tag_levels = 'A')
# ggsave("figs/leaf_gli.png",dpi = 600, height = 8, width =8)Relationship sev indices
rgb_gg_pi = all_data_PI %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_pi## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_pi = all_data_PI %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_pi## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_pi,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_pi,
nrow = 2,
rel_heights = c(0.5,1))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_pi.png", dpi = 500, height = 8, width = 10)cor_PI = all_data_PI %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = round(cor.test(mean,sev, method = "spearman")$p.value,4)) %>%
arrange(-cor)## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties## `summarise()` ungrouping output (override with `.groups` argument)cor_PI## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 BGI 0.762 0
## 2 Blue 0.708 0
## 3 BI 0.399 0
## 4 gray 0.356 0
## 5 Red 0.295 0
## 6 Green 0.267 0.0001
## 7 SCI 0.106 0.136
## 8 HUE 0.098 0.169
## 9 VARI -0.069 0.332
## 10 NGRDI -0.106 0.136
## 11 HI -0.161 0.0224
## 12 GLI -0.492 0
## 13 SI -0.724 0Spread df
all_data_spread = all_data_PI %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean)
all_data_spread## # A tibble: 200 x 15
## file sev BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 foto~ 13.6 0.254 57.1 20.7 0.304 64.0 74.1 0.511 -0.663 0.0975 60.9
## 2 foto~ 11.8 0.227 54.3 18.0 0.311 60.8 70.3 0.550 -0.682 0.0919 58.6
## 3 foto~ 0.95 0.0561 45.4 4.38 0.477 51.6 64.9 0.282 -0.658 0.209 43.4
## 4 foto~ 15.2 0.313 60.2 26.0 0.274 67.4 77.2 0.499 -0.665 0.0914 64.0
## 5 foto~ 7.81 0.289 55.1 25.5 0.391 62.3 76.5 -0.194 -0.853 0.248 48.4
## 6 foto~ 1.39 0.360 56.3 28.6 0.287 63.0 73.5 0.183 -0.783 0.138 55.5
## 7 foto~ 2.08 0.318 52.9 25.0 0.313 59.2 69.6 0.146 -0.686 0.148 51.9
## 8 foto~ 2.03 0.204 52.9 16.2 0.338 59.7 70.4 0.499 -0.660 0.118 55.3
## 9 foto~ 0.6 0.199 51.7 15.5 0.336 58.2 68.3 0.507 -0.682 0.111 54.5
## 10 foto~ 1.74 0.281 49.6 20.6 0.299 55.7 64.7 0.446 -0.620 0.110 51.4
## # ... with 190 more rows, and 3 more variables: SCI <dbl>, SI <dbl>, VARI <dbl>GBM
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 80.
## There were 13 predictors of which 13 had non-zero influence.sqrt(min(gbm.fit$cv.error))## [1] 14.0629gbm.perf(gbm.fit, method = "cv")
## [1] 80# find index for n trees with minimum CV errormin_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])## [1] 14.0629# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )## Using 80 trees...sqrt(mean(((pred)-all_data_spread$sev[-train])^2))## [1] 13.84988CCC((pred), all_data_spread$sev[-train])$rho.c$est## [1] 0.7509533plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Testing various hyperparameters
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)## [1] 192# randomize data
set.seed(123)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC((pred), all_data_spread$sev[-train])$rho.c$est
}## Using 4982 trees...## Using 936 trees...## Using 290 trees...## Using 18 trees...## Using 2198 trees...## Using 215 trees...## Using 26 trees...## Using 6 trees...## Using 2231 trees...## Using 296 trees...## Using 25 trees...## Using 6 trees...## Using 2231 trees...## Using 329 trees...## Using 30 trees...## Using 6 trees...## Using 4669 trees...## Using 496 trees...## Using 42 trees...## Using 269 trees...## Using 3338 trees...## Using 307 trees...## Using 30 trees...## Using 9 trees...## Using 3338 trees...## Using 343 trees...## Using 25 trees...
##
## Using 25 trees...## Using 3338 trees...## Using 343 trees...## Using 25 trees...
##
## Using 25 trees...## Using 4617 trees...## Using 496 trees...## Using 47 trees...## Using 24 trees...## Using 4601 trees...## Using 3363 trees...## Using 52 trees...## Using 147 trees...## Using 4601 trees...## Using 3363 trees...## Using 52 trees...## Using 147 trees...## Using 4601 trees...## Using 3363 trees...## Using 52 trees...## Using 147 trees...## Using 4991 trees...## Using 936 trees...## Using 83 trees...## Using 23 trees...## Using 2283 trees...## Using 275 trees...## Using 29 trees...## Using 6 trees...## Using 2181 trees...## Using 206 trees...## Using 23 trees...## Using 6 trees...## Using 2200 trees...## Using 210 trees...## Using 23 trees...## Using 6 trees...## Using 4996 trees...## Using 725 trees...## Using 82 trees...## Using 50 trees...## Using 2792 trees...## Using 269 trees...## Using 21 trees...## Using 9 trees...## Using 2781 trees...## Using 298 trees...## Using 74 trees...## Using 51 trees...## Using 2781 trees...## Using 277 trees...## Using 21 trees...## Using 9 trees...## Using 4661 trees...## Using 495 trees...## Using 50 trees...## Using 10 trees...## Using 4437 trees...## Using 367 trees...## Using 50 trees...## Using 10 trees...## Using 4246 trees...## Using 356 trees...## Using 52 trees...## Using 10 trees...## Using 4246 trees...## Using 356 trees...## Using 52 trees...## Using 10 trees...## Using 4995 trees...## Using 954 trees...## Using 59 trees...## Using 32 trees...## Using 2542 trees...## Using 264 trees...## Using 22 trees...## Using 7 trees...## Using 2186 trees...## Using 214 trees...## Using 22 trees...## Using 7 trees...## Using 2047 trees...## Using 250 trees...## Using 22 trees...## Using 5 trees...## Using 4995 trees...## Using 1195 trees...## Using 251 trees...## Using 32 trees...## Using 2467 trees...## Using 285 trees...## Using 22 trees...## Using 6 trees...## Using 2663 trees...## Using 238 trees...## Using 22 trees...## Using 6 trees...## Using 2612 trees...## Using 214 trees...## Using 25 trees...## Using 7 trees...## Using 4995 trees...## Using 689 trees...## Using 71 trees...## Using 22 trees...## Using 3167 trees...## Using 285 trees...## Using 36 trees...## Using 10 trees...## Using 3846 trees...## Using 344 trees...## Using 35 trees...## Using 9 trees...## Using 3846 trees...## Using 344 trees...## Using 35 trees...## Using 9 trees...## Using 4006 trees...## Using 3446 trees...## Using 327 trees...## Using 72 trees...## Using 2391 trees...## Using 237 trees...## Using 31 trees...## Using 7 trees...## Using 2297 trees...## Using 232 trees...## Using 19 trees...## Using 7 trees...## Using 2519 trees...## Using 219 trees...## Using 26 trees...## Using 6 trees...## Using 5000 trees...## Using 1416 trees...## Using 139 trees...## Using 31 trees...## Using 3049 trees...## Using 329 trees...## Using 37 trees...## Using 11 trees...## Using 2371 trees...## Using 236 trees...## Using 24 trees...## Using 7 trees...## Using 2302 trees...## Using 232 trees...## Using 21 trees...## Using 7 trees...## Using 4999 trees...## Using 3942 trees...## Using 371 trees...## Using 138 trees...## Using 3041 trees...## Using 312 trees...## Using 23 trees...## Using 8 trees...## Using 3754 trees...## Using 348 trees...## Using 35 trees...## Using 9 trees...## Using 4014 trees...## Using 405 trees...## Using 35 trees...## Using 10 trees...best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.300 1 15 0.50 24
## 2 0.300 3 15 0.65 10
## 3 0.300 5 15 0.65 10
## 4 0.300 6 15 0.65 10
## 5 0.300 1 15 0.80 22
## 6 0.300 1 15 1.00 138
## 7 0.010 1 15 0.65 495
## 8 0.010 1 15 0.80 689
## 9 0.001 1 15 0.65 4661
## 10 0.001 1 15 0.50 4617
## min_RMSE CCC
## 1 12.22198 0.7088796
## 2 11.82089 0.7009918
## 3 12.21880 0.7008317
## 4 12.21880 0.7008317
## 5 11.57058 0.6999291
## 6 12.01776 0.6954443
## 7 12.60668 0.6931396
## 8 12.49874 0.6891423
## 9 12.58996 0.6836161
## 10 12.76863 0.6821743# gbm.tune$fitBest model
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)Relative influence
par(mar = c(5, 8, 1, 1))
summary_gbm_plb = summary(
gbm.fit.final,
cBars = 13,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_plb = summary_gbm_plb %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_plb
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "BGI", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( BGI,(yhat)))+
geom_line()
LIME
library(lime)model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
Prediction
# predict values for test data
pred <- predict(gbm.fit.final, n.trees = gbm.fit.final$n.trees, all_data_spread[-train,])
# results
caret::RMSE(pred, all_data_spread[-train,]$sev)## [1] 13.38866CCC(pred, all_data_spread$sev[-train])$rho.c$est## [1] 0.7088796cor(pred, all_data_spread$sev[-train])^2## [1] 0.5509299accuracy_pi =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_pi## RMSE r s.shift l.shift C.b CCC CIS
## 1 13.38866 0.7422465 1.285102 -0.1757541 0.9550461 0.7088796 0.53, 0.83plot
conc_pi = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)## `geom_smooth()` using formula 'y ~ x'Calonectria leaf blight
pics<-list.files("./pics/01-Calonectria_leaf_bligth")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-Calonectria_leaf_bligth/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=175, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_calonec.txt")box = read.table("data/indexes_calonec.txt")data_calo_load = gsheet2tbl("https://docs.google.com/spreadsheets/d/1Nqd9NqbynjR1bI2wrYdsKXZT3HACT8CVqyfY5n70DhI/edit?usp=sharing") %>%
mutate(file = as.character(file)) %>%
dplyr::select(file,area_total,area_doente_roxa,area_doente,sev_roxa,sev)
head(data_calo_load)## # A tibble: 6 x 6
## file area_total area_doente_roxa area_doente sev_roxa sev
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 411776 54374 9384 13.2 2.28
## 2 2 415627 81757 55557 19.7 13.4
## 3 3 761523 569173 413002 74.7 54.2
## 4 4 798787 708346 594451 88.7 74.4
## 5 5 657200 219361 85449 33.4 13.0
## 6 6 784817 628389 628389 80.1 80.1data_calo = box %>%
separate(leaf, into=c("file","format"), sep =".jpg") %>%
dplyr::select(-format) %>%
full_join(data_calo_load, by="file") %>%
mutate(sev=sev_roxa)
# data_calo
length(unique(data_calo$sev))## [1] 300summary(data_calo$sev)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8715 4.4431 9.8264 16.2402 20.1989 95.4470Images
hist_sev_calo = data_calo %>%
pivot_wider(id_col = c(file,sev_roxa),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev_roxa))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))EX.L1<-stack(paste("./pics/01-Calonectria_leaf_bligth/","57.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)## [1] "3 layers available"# EX1.Indices<- fieldIndex(mosaic = EX.L1,
# index = index,
# myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=175, cropAbove=T, plot = F)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)## [1] "3 layers available"# plot(EX.L4$HUE)rgb_fig_calo = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))## Coordinate system already present. Adding new coordinate system, which will replace the existing one.gli_fig_calo = as.data.frame(EX.L4$HUE, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = HUE))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
rgb_fig_calo+ gli_fig_calo
rgb_fig_sbr + gli_fig_sbr + hist_sev_sbr +
rgb_fig_xy + gli_fig_xy +hist_sev_xy+
rgb_fig_calo + gli_fig_calo + hist_sev_calo+
rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
rgb_fig_pi + gli_fig_pi + hist_sev_pi+
plot_layout(widths = c(1, 1, 1),
heights = c(1,1,1,1,1))+
plot_annotation(tag_levels = 'A')&
theme(legend.key.size = unit(3, 'mm'),
legend.text = element_text(size =6))## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
ggsave("figs/leaf_gli.png",dpi = 600, height = 9, width =7)## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).Relationship sev indices
rgb_gg_calo = data_calo %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev_roxa, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_calo## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_calo = data_calo %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev_roxa, label = file)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_calo## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_calo,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_calo,
nrow = 2,
rel_heights = c(0.5,1))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_calo.png", dpi = 500, height = 8, width = 10)cor_calo = data_calo %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev_roxa, method = "spearman")$estimate,3),
P_value = round(cor.test(mean,sev_roxa, method = "spearman")$p.value,4)) %>%
arrange(-cor)## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties## `summarise()` ungrouping output (override with `.groups` argument)cor_calo## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 HUE 0.982 0
## 2 SCI 0.773 0
## 3 Red 0.754 0
## 4 SI 0.678 0
## 5 HI 0.621 0
## 6 BI 0.568 0
## 7 gray 0.479 0
## 8 BGI 0.405 0
## 9 Blue 0.36 0
## 10 Green 0.065 0.264
## 11 GLI -0.736 0
## 12 VARI -0.743 0
## 13 NGRDI -0.773 0Spread df
all_data_spread = data_calo %>%
pivot_wider(id_col = c(file,sev_roxa),
names_from = index,
values_from = mean) %>%
mutate(sev = sev_roxa) %>%
dplyr::select(-sev_roxa)
all_data_spread## # A tibble: 300 x 15
## file BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.688 87.8 75.2 0.225 93.8 111. -0.985 -1.21 0.265 66.6
## 2 10 0.733 152. 109. 0.00994 155. 149. 2.75 1.30 -0.101 185.
## 3 100 0.678 95.3 82.5 0.231 102. 122. -1.04 -1.36 0.274 71.5
## 4 101 0.661 97.5 79.9 0.204 105. 122. -0.490 -1.27 0.203 82.7
## 5 102 0.631 87.3 71.9 0.257 94.6 114. -1.05 -1.35 0.291 65.1
## 6 103 0.599 90.4 69.1 0.243 98.7 117. -0.516 -1.23 0.233 74.2
## 7 104 0.573 89.2 69.9 0.307 97.8 122. -1.11 -1.45 0.344 61.3
## 8 105 0.768 92.9 77.9 0.0973 97.0 103. 1.29 -0.417 0.0721 92.9
## 9 106 0.645 102. 79.6 0.187 110. 124. 0.765 -1.01 0.161 93.1
## 10 107 0.660 88.9 76.0 0.241 96.1 115. -1.40 -1.45 0.279 66.6
## # ... with 290 more rows, and 4 more variables: SCI <dbl>, SI <dbl>,
## # VARI <dbl>, sev <dbl>GBM
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 185.
## There were 13 predictors of which 13 had non-zero influence.sqrt(min(gbm.fit$cv.error))## [1] 4.651259gbm.perf(gbm.fit, method = "cv")
## [1] 185# find index for n trees with minimum CV errormin_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])## [1] 4.651259# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )## Using 185 trees...sqrt(mean(((pred)-all_data_spread$sev[-train])^2))## [1] 4.170014CCC((pred), all_data_spread$sev[-train])$rho.c$est## [1] 0.9735623plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Testing various hyperparameters
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)## [1] 192# randomize data
set.seed(1234)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC((pred), all_data_spread$sev[-train])$rho.c$est
}## Using 5000 trees...## Using 4835 trees...## Using 555 trees...## Using 104 trees...## Using 5000 trees...## Using 4997 trees...## Using 1871 trees...## Using 4957 trees...## Using 5000 trees...## Using 4957 trees...## Using 1689 trees...## Using 1683 trees...## Using 5000 trees...## Using 4987 trees...## Using 1968 trees...## Using 12 trees...## Using 4996 trees...## Using 5000 trees...## Using 546 trees...## Using 42 trees...## Using 4996 trees...## Using 1376 trees...## Using 115 trees...## Using 40 trees...## Using 4996 trees...## Using 1377 trees...## Using 56 trees...## Using 24 trees...## Using 4996 trees...## Using 1376 trees...## Using 58 trees...## Using 4114 trees...## Using 4293 trees...## Using 1642 trees...## Using 201 trees...## Using 81 trees...## Using 4996 trees...## Using 661 trees...## Using 47 trees...## Using 42 trees...## Using 4996 trees...## Using 863 trees...## Using 47 trees...## Using 31 trees...## Using 4996 trees...## Using 863 trees...## Using 47 trees...## Using 31 trees...## Using 5000 trees...## Using 4837 trees...## Using 395 trees...## Using 114 trees...## Using 5000 trees...## Using 2697 trees...## Using 253 trees...## Using 394 trees...## Using 4998 trees...## Using 2692 trees...## Using 175 trees...## Using 4982 trees...## Using 4998 trees...## Using 3721 trees...## Using 456 trees...## Using 244 trees...## Using 5000 trees...## Using 3831 trees...## Using 461 trees...## Using 97 trees...## Using 5000 trees...## Using 1073 trees...## Using 115 trees...## Using 4999 trees...## Using 5000 trees...## Using 712 trees...## Using 115 trees...## Using 35 trees...## Using 5000 trees...## Using 706 trees...## Using 115 trees...## Using 37 trees...## Using 4974 trees...## Using 1640 trees...## Using 147 trees...## Using 96 trees...## Using 5000 trees...## Using 822 trees...## Using 46 trees...## Using 30 trees...## Using 5000 trees...## Using 955 trees...## Using 46 trees...## Using 37 trees...## Using 5000 trees...## Using 955 trees...## Using 46 trees...## Using 30 trees...## Using 5000 trees...## Using 3047 trees...## Using 379 trees...## Using 62 trees...## Using 4998 trees...## Using 1315 trees...## Using 253 trees...## Using 105 trees...## Using 4998 trees...## Using 1611 trees...## Using 132 trees...## Using 26 trees...## Using 4998 trees...## Using 4957 trees...## Using 128 trees...## Using 26 trees...## Using 5000 trees...## Using 4828 trees...## Using 1231 trees...## Using 1735 trees...## Using 5000 trees...## Using 2842 trees...## Using 3829 trees...## Using 14 trees...## Using 5000 trees...## Using 525 trees...## Using 79 trees...## Using 29 trees...## Using 5000 trees...## Using 539 trees...## Using 55 trees...## Using 23 trees...## Using 4999 trees...## Using 4606 trees...## Using 331 trees...## Using 160 trees...## Using 5000 trees...## Using 1046 trees...## Using 85 trees...## Using 34 trees...## Using 5000 trees...## Using 870 trees...## Using 89 trees...## Using 38 trees...## Using 5000 trees...## Using 870 trees...## Using 85 trees...## Using 31 trees...## Using 5000 trees...## Using 4991 trees...## Using 843 trees...## Using 262 trees...## Using 5000 trees...## Using 3072 trees...## Using 213 trees...## Using 53 trees...## Using 5000 trees...## Using 1799 trees...## Using 242 trees...## Using 51 trees...## Using 5000 trees...## Using 2183 trees...## Using 2385 trees...## Using 44 trees...## Using 5000 trees...## Using 4104 trees...## Using 582 trees...## Using 1097 trees...## Using 5000 trees...## Using 588 trees...## Using 63 trees...## Using 11 trees...## Using 4999 trees...## Using 548 trees...## Using 52 trees...## Using 15 trees...## Using 5000 trees...## Using 584 trees...## Using 58 trees...## Using 14 trees...## Using 5000 trees...## Using 1121 trees...## Using 133 trees...## Using 156 trees...## Using 3806 trees...## Using 382 trees...## Using 42 trees...## Using 11 trees...## Using 3959 trees...## Using 400 trees...## Using 41 trees...## Using 12 trees...## Using 3830 trees...## Using 394 trees...## Using 37 trees...## Using 12 trees...best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.300 3 5 1 53
## 2 0.100 1 5 1 843
## 3 0.010 1 5 1 4991
## 4 0.300 1 5 1 262
## 5 0.100 3 5 1 213
## 6 0.001 5 5 1 5000
## 7 0.001 6 5 1 5000
## 8 0.300 6 5 1 44
## 9 0.300 5 5 1 51
## 10 0.001 3 5 1 5000
## min_RMSE CCC
## 1 3.188960 0.9921566
## 2 2.913543 0.9917062
## 3 3.008064 0.9912300
## 4 3.429057 0.9907054
## 5 2.960664 0.9906812
## 6 3.378191 0.9904229
## 7 3.403406 0.9903718
## 8 3.064615 0.9901287
## 9 3.177959 0.9900388
## 10 3.297901 0.9900336# gbm.tune$fitBest model
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)Relative influence
par(mar = c(5, 8, 1, 1))
summary_gbm_calo = summary(
gbm.fit.final,
cBars = 13,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_calo = summary_gbm_calo %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_calo
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "HUE", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( HUE,(yhat)))+
geom_line()
LIME
library(lime)model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
Prediction
# predict values for test data
pred <- predict(gbm.fit.final, n.trees = gbm.fit.final$n.trees, all_data_spread[-train,])
# results
caret::RMSE(pred, all_data_spread[-train,]$sev)## [1] 2.358233CCC(pred, all_data_spread$sev[-train])$rho.c$est## [1] 0.9921566cor(pred, all_data_spread$sev[-train])^2## [1] 0.9872815accuracy_calo = data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_calo## RMSE r s.shift l.shift C.b CCC CIS
## 1 2.358233 0.9936204 1.047502 0.02822577 0.9985268 0.9921566 0.99, 1plot
conc_calo = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)## `geom_smooth()` using formula 'y ~ x'Correlation
ind_order = c("Red","Green","Blue","BI","SCI","GLI","HI","NGRDI","SI","VARI","HUE","BGI","gray")
bind_rows(
cor_sbr %>% mutate(disease="SBR"),
cor_calo %>% mutate(disease="CLB"),
cor_xy %>% mutate(disease="NtXf"),
cor_wlb %>% mutate(disease="WLB"),
cor_PI %>% mutate(disease="PLB")) %>%
mutate(sig = case_when(P_value <0.05 ~ " ",
P_value >0.05 ~ "P>0.05")) %>%
mutate(disease = factor(disease, levels = c("PLB", "WLB","NtXf","CLB", "SBR")),
index = factor(index, levels = ind_order)) %>%
ggplot(aes(index,disease, fill = cor, label = round(cor,3)))+
geom_tile()+
geom_text(size =3.5)+
geom_point(aes(index,disease, color = sig), shape = "X", size =8, alpha = 0.6)+
# scale_fill_gradient2(low = "darkred", mid = NA, high = "darkgreen")+
scale_fill_distiller(palette = "RdBu", direction = 1)+
scale_color_manual(values = c(NA,"black"))+
theme_half_open()+
labs(x = "",
y = "",
fill = " r",
color ="")## Warning: Removed 59 rows containing missing values (geom_point).
ggsave("figs/corr.png",dpi = 600, height = 3.5, width =10)## Warning: Removed 59 rows containing missing values (geom_point).Relative influence combo
summary_gbm_sbr## var rel.inf
## HUE HUE 87.88801884
## NGRDI NGRDI 6.28297306
## SCI SCI 4.37465525
## GLI GLI 0.47741094
## HI HI 0.39728015
## VARI VARI 0.15791219
## Red Red 0.15652889
## BI BI 0.11433955
## Blue Blue 0.06497336
## Green Green 0.03215579
## SI SI 0.02229384
## BGI BGI 0.02060409
## gray gray 0.01085406summary_gbm_calo## var rel.inf
## HUE HUE 94.03402438
## SCI SCI 4.09247026
## BI BI 0.81359184
## BGI BGI 0.28727216
## SI SI 0.18018170
## NGRDI NGRDI 0.14039540
## Blue Blue 0.13733611
## HI HI 0.09923759
## gray gray 0.06551079
## Green Green 0.05420577
## GLI GLI 0.04420588
## Red Red 0.03073479
## VARI VARI 0.02083332summary_gbm_xy## var rel.inf
## HUE HUE 45.0933820
## HI HI 19.2008467
## SI SI 16.0327582
## BGI BGI 7.1783997
## Green Green 5.0459348
## NGRDI NGRDI 3.7484209
## BI BI 2.1717994
## GLI GLI 0.7792653
## Red Red 0.7491929
## VARI VARI 0.0000000
## Blue Blue 0.0000000
## gray gray 0.0000000
## SCI SCI 0.0000000summary_gbm_wlb## var rel.inf
## VARI VARI 28.4651011
## HI HI 22.7166812
## Blue Blue 17.7541396
## GLI GLI 10.8559511
## SCI SCI 6.9322339
## HUE HUE 4.4167122
## NGRDI NGRDI 4.1782350
## BGI BGI 1.4914608
## SI SI 1.3595593
## Red Red 0.8233217
## BI BI 0.4903142
## Green Green 0.2792345
## gray gray 0.2370553summary_gbm_plb## var rel.inf
## SI SI 34.172257
## BGI BGI 23.132425
## HUE HUE 12.898477
## Green Green 9.907507
## Blue Blue 9.040377
## BI BI 3.744766
## GLI GLI 2.959053
## HI HI 2.437804
## gray gray 1.707335
## NGRDI NGRDI 0.000000
## VARI VARI 0.000000
## Red Red 0.000000
## SCI SCI 0.000000rel_sbr + labs(title = paste("Soybean rust"))+
rel_calo +labs(title = paste("Calonectria leaf blight"))+
rel_wlb +labs(title = paste("Wheat leaf blast"))+
rel_xy +labs(title = expression(bolditalic("N. tabacum-X. fastidiosa")))+
rel_plb +labs(title = paste("Potato late blight"))+guide_area()+
plot_layout(ncol =3, guides = "collect")+
plot_annotation(tag_levels = 'A')&
theme_minimal_grid(font_size = 9)&
theme(plot.title = element_text(size =8))&
xlim(0,100)
ggsave("figs/relative_influence_combo.png", dpi = 300, height = 5, width = 6)Agreement
conc_sbr+labs(title = paste("Soybean rust"),
subtitle = paste("RMSE = ",round(accuracy_sbr$RMSE,2),", ",
"CCC = ",round(accuracy_sbr$CCC,3)))+
conc_calo+labs(title = paste("Calonectria leaf blight"),
subtitle = paste("RMSE = ",round(accuracy_calo$RMSE,2),", ",
"CCC = ",round(accuracy_calo$CCC,3)))+
conc_wlb+labs(title = paste("Wheat leaf blast"),
subtitle = paste("RMSE = ",round(accuracy_wlb$RMSE,2),", ",
"CCC = ",round(accuracy_wlb$CCC,3)))+
conc_xy+labs(title = expression(bolditalic("N. tabacum-X. fastidiosa")),
subtitle = paste("RMSE = ",round(accuracy_xy$RMSE,2),", ",
"CCC = ",round(accuracy_xy$CCC,3)))+
conc_pi+labs(title = paste("Potato late blight"),
subtitle = paste("RMSE = ",round(accuracy_pi$RMSE,2),", ",
"CCC = ",round(accuracy_pi$CCC,3)))+
plot_layout(ncol = 3,
widths = c(1, 1,1),
heights = c(1,1))+
plot_annotation(tag_levels = 'A')&
theme_minimal_grid(font_size = 10)&
theme(plot.title = element_text(size =10, face ="bold"),
plot.subtitle = element_text(size =10, face = "plain"))## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
ggsave("figs/concordance.png", dpi = 600, height = 6, width = 8)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'RGB bands by leaf
`
density_rgb = function(file_dir, limiar, index_cut, cropAbove=T,
left = 0.5, bottom = 0.5, right=1.1, top=1.1){
SBR_low_EX.L1<-stack(paste(file_dir))
SBR_low_EX.L1<-aggregate(SBR_low_EX.L1, fact=5)
SBR_low_EX.L2<-fieldMask(mosaic=SBR_low_EX.L1, myIndex = c(index_cut), cropValue=limiar, cropAbove=cropAbove, plot = F)
cut = mask(SBR_low_EX.L1, SBR_low_EX.L2$newMosaic)
SBR_low_EX.L4<-fieldIndex(mosaic=cut,
# index =index,
plot =F)
SBR_low = RStoolbox::ggRGB(SBR_low_EX.L2$newMosaic,r = 1,g = 2,b = 3)+
theme_map()+coord_fixed()+
theme(panel.background = element_rect(color = NA, fill = NA),
)
RLB_low_comb = data.frame(R = as.data.frame(SBR_low_EX.L4$Red),
G = as.data.frame(SBR_low_EX.L4$Blue),
B = as.data.frame(SBR_low_EX.L4$Green)) %>%
na.omit() %>%
pivot_longer(1:3,names_to = "band", values_to = "value") %>%
# ggplot(aes(value, fill = band))+
# geom_density(color = NA, alpha = 0.8)+
ggplot(aes(value))+
stat_slab(aes(fill = band), alpha = 0.6)+
stat_pointinterval(aes(color = band),
position = position_dodge(width = .6, preserve = "single"),
# color = "gray40",
.width = c(0,0.95))+
scale_fill_manual(values = c("blue", "green","red" ))+
scale_color_manual(values = c("blue", "green","red" ))+
scale_x_continuous(breaks = seq(0,250,50), limits = c(0,300))+
theme_minimal()+
theme(axis.text.y = element_blank(),
panel.grid = element_blank(),
axis.ticks.x = element_line(color = "gray"),
axis.line.x = element_line(color = "gray"))+
labs(x = "Pixel intensity",
y = "",
fill ="")+
guides(color =F)+
inset_element(SBR_low, left, bottom, right, top, align_to = 'full')
RLB_low_comb$patches$layout$widths <- 1
RLB_low_comb$patches$layout$heights <- 1
RLB_low_comb
}SBR
SBR_low = density_rgb("./pics/01-soybean-rust-bg-blue/Ferrugem 52_Median.jpg",
limiar = 100, index_cut = "Blue",
left =0.55,bottom = 0.55,right = 1.1,top = 1.1)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.SBR_high = density_rgb("./pics/01-soybean-rust-bg-blue/Ferrugem 49_Median.jpg",
limiar = 100, index_cut = "Blue",
left =0.55,bottom = 0.55,right = 1.1,top = 1.1)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.SBR_low+SBR_high
Calo
calo_low = density_rgb("./pics/01-Calonectria_leaf_bligth/108.jpg",
limiar = 175, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.calo_high = density_rgb("./pics/01-Calonectria_leaf_bligth/153.jpg",
limiar = 175, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.calo_low+calo_high
## XY
xy_low = density_rgb("./pics/01-Xylella-tobacco-bg-white/68.jpg",
limiar = 200, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.xy_high = density_rgb("./pics/01-Xylella-tobacco-bg-white/82.jpg",
limiar = 200, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.xy_low+xy_high
WLB
wlb_low = density_rgb("./pics/01-Wheat_leaf_blast/G_108_R3.jpg",
limiar = 180, index_cut = "Blue")## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.wlb_high = density_rgb("./pics/01-Wheat_leaf_blast/T_239_R1.jpg",
limiar = 180, index_cut = "Blue")## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.wlb_low+wlb_high
PLB
plb_low = density_rgb("./pics/01-potato_late_bligh/PI24_6B.png",
limiar = 1, index_cut = "Red", cropAbove = F,
0.55,0.55,1.2,1.1)## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.plb_high = density_rgb("./pics/01-potato_late_bligh/PI07_2A.png",
limiar = 1, index_cut = "Red", cropAbove = F,
0.55,0.55,1.2,1.1)## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.plb_low+plb_high
## COMBO RGB
tags = c("A", "", "B", "", "C", "","D","","E","","F","","G","","H","","I","","J","")
SBR_low+SBR_high+
calo_low+calo_high+
xy_low+xy_high+
wlb_low+wlb_high+
plb_low+plb_high+
plot_layout(widths = c(1, 1),
heights = c(1,1,1,1,1),
tag_level = "keep",
guides = 'collect')+
plot_annotation(tag_levels = list(tags))&
theme(legend.key.size = unit(5, 'mm'),
legend.position = "bottom",
legend.text = element_text(size =10))
ggsave("figs/RGB_dens.png",dpi = 600, height = 8, width =7)new
density_rgb2 = function(
file_low = "./pics/01-soybean-rust-bg-blue/Ferrugem 52_Median.jpg",
file_high = "./pics/01-soybean-rust-bg-blue/Ferrugem 49_Median.jpg",
limiar = 100,
index_cut = "Blue",
cropAbove=T,
title = "Soybean Rust",face = "plain"){
#-------------------------------------------------------------------------------------------------
SBR_low_EX.L1<-stack(paste(file_low ))
SBR_low_EX.L1<-aggregate(SBR_low_EX.L1, fact=5)
SBR_low_EX.L2<-fieldMask(mosaic=SBR_low_EX.L1, myIndex = c(index_cut), cropValue=limiar, cropAbove=cropAbove, plot = F)
cut = mask(SBR_low_EX.L1, SBR_low_EX.L2$newMosaic)
SBR_low_EX.L4<-fieldIndex(mosaic=cut,
# index =index,
plot =F)
SBR_low = RStoolbox::ggRGB(SBR_low_EX.L2$newMosaic,r = 1,g = 2,b = 3)+
theme_map()+coord_fixed()+
theme(panel.background = element_rect(color = NA, fill = NA),
plot.title = element_text(size=8, face = "plain"))+
labs(title = "Low")
SBR_low_comb = data.frame(R = as.data.frame(SBR_low_EX.L4$Red),
G = as.data.frame(SBR_low_EX.L4$Blue),
B = as.data.frame(SBR_low_EX.L4$Green)) %>%
na.omit() %>%
pivot_longer(1:3,names_to = "band", values_to = "value") %>%
mutate(sev="Low")
#-------------------------------------------------------------------------------------------------
SBR_high_EX.L1<-stack(paste(file_high))
SBR_high_EX.L1<-aggregate(SBR_high_EX.L1, fact=5)
SBR_high_EX.L2<-fieldMask(mosaic=SBR_high_EX.L1, myIndex = c(index_cut), cropValue=limiar, cropAbove=cropAbove, plot = F)
cut = mask(SBR_high_EX.L1, SBR_high_EX.L2$newMosaic)
SBR_high_EX.L4<-fieldIndex(mosaic=cut,
# index =index,
plot =F)
SBR_high = RStoolbox::ggRGB(SBR_high_EX.L2$newMosaic,r = 1,g = 2,b = 3)+
theme_map()+coord_fixed()+
theme(panel.background = element_rect(color = NA, fill = NA),
plot.title = element_text(size=8, face = "plain"))+
labs(title = "High")
SBR_high_comb = data.frame(R = as.data.frame(SBR_high_EX.L4$Red),
G = as.data.frame(SBR_high_EX.L4$Blue),
B = as.data.frame(SBR_high_EX.L4$Green)) %>%
na.omit() %>%
pivot_longer(1:3,names_to = "band", values_to = "value") %>%
mutate(sev="High")
#-------------------------------------------------------------------------------------------------
all_comb = bind_rows(SBR_low_comb,SBR_high_comb) %>%
mutate(sev =factor(sev, levels =c("Low","High")))
#-------------------------------------------------------------------------------------------------
all_comb %>%
ggplot(aes(value,sev))+
stat_slab(aes(fill = band), alpha = 0.7)+
stat_pointinterval(aes(color = band),
position = position_dodge(width = .5, preserve = "single"),
# color = "gray40",
.width = c(0,0.95))+
scale_fill_manual(values = c("blue", "green","red" ))+
scale_color_manual(values = c("blue", "green","red" ))+
theme_minimal()+
xlim(0,255)+
theme(panel.grid = element_blank(),
plot.title = element_text(size = 10, face = face),
axis.ticks = element_line(color = "gray"),
axis.line = element_line(color = "gray"))+
labs(x = "Pixel intensity",
y = "Severity",
fill ="",
title = paste(title))+
guides(color =F)#+
#SBR_low+SBR_high+
#plot_layout(widths = c(1, .2,.2))
}plots
SBR_dist = density_rgb2()## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.calo_dist =density_rgb2(file_low ="./pics/01-Calonectria_leaf_bligth/108.jpg",
file_high = "./pics/01-Calonectria_leaf_bligth/153.jpg",
limiar = 175,index_cut = "Blue",cropAbove=T,
title = "Calonectria leaf blight")## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.xy_dist = density_rgb2(file_low ="./pics/01-Xylella-tobacco-bg-white/68.jpg",
file_high = "./pics/01-Xylella-tobacco-bg-white/82.jpg",
limiar = 200,index_cut = "Blue", cropAbove=T,
title = "N. tabacum-X. fastidiosa", face = "italic")## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.wlb_dist = density_rgb2(file_low ="./pics/01-Wheat_leaf_blast/G_108_R3.jpg",
file_high = "./pics/01-Wheat_leaf_blast/T_239_R1.jpg",
limiar = 180,index_cut = "Blue",cropAbove=T,
title = "Wheat leaf blast")## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.plb_dist = density_rgb2(file_low ="./pics/01-potato_late_bligh/PI24_6B.png",
file_high = "./pics/01-potato_late_bligh/PI07_2A.png",
limiar = 1,index_cut = "Red",cropAbove=F,
title = "Potato late blight")## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"## Coordinate system already present. Adding new coordinate system, which will replace the existing one.combo
(SBR_dist+calo_dist+xy_dist+wlb_dist+plb_dist+guide_area())+
plot_layout(nrow = 3,
ncol = 2,
tag_level = "keep",
# widths = c(1,1,1,1,1),
guides = 'collect')+
plot_annotation(tag_levels = "A")+
# plot_annotation(tag_levels = list(tags2))&
theme(legend.key.size = unit(5, 'mm'),
legend.position = "none",
legend.text = element_text(size =10))
ggsave("figs/RGB_dens2.png",dpi = 600, height = 7, width =6)